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aconitus</i> Dönitz, 1902<p><small>Predicted distribution of <i>Anopheles (Cel.) aconitus</i> Dönitz, 1902</p></small><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) aconitus</i> Dönitz, 1902.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_aconitusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_aconitusExplorer__2010_Anopheles_aconitusRectifiedGridCoverage71.9583282470703 -11.0416669845581128.25 29.916667938232471.95833333332325 -11.041666666662636128.2499999999904 29.91666666667102<i>Anopheles albimanus</i> Wiedemann, 1820<p><small>Predicted distribution of <i>Anopheles (Nys.) albimanus</i> Wiedemann, 1820</p></small><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) albimanus</i> Wiedemann, 1820.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_albimanusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_albimanusExplorer__2010_Anopheles_albimanusRectifiedGridCoverage-110.833335876465 -12.3333339691162-62.9166641235352 28.4166679382324-110.8333333333361 -12.33333333332925-62.91666666666905 28.416666666671077<i>Anopheles albitarsis</i> species complex<p><small>Predicted distribution of <i>Anopheles (Nys.) albitarsis</i> species complex</p></small><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) albitarsis</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_albitarsisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_albitarsisExplorer__2010_Anopheles_albitarsisRectifiedGridCoverage-85.9583358764648 -36.7916679382324-32.3333320617676 12.5000009536743-85.9583333333371 -36.791666666661605-32.33333333333667 12.50000000000545<i>Anopheles annularis</i> van der Wulp, 1884<p><small>Predicted distribution of <i>Anopheles (Cel.) annularis</i> van der Wulp, 1884</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) annularis</i> van der Wulp, 1884.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_annularisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_annularisExplorer__2010_Anopheles_annularisRectifiedGridCoverage71.6666641235352 -11.0416669845581128.25 30.375001907348671.6666666666566 -11.041666666662636128.2499999999904 30.375000000004363<i>Anopheles aquasalis</i> Curry, 1932<p><small>Predicted distribution of <i>Anopheles (Nys.) aquasalis</i> Curry, 1932</small><p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) aquasalis</i> Curry, 1932.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_aquasalisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_aquasalisExplorer__2010_Anopheles_aquasalisRectifiedGridCoverage-84.4583358764648 -24.125-32.375 18.6250019073486-84.45833333333715 -24.124999999995453-32.3750000000034 18.62500000000489<i>Anopheles arabiensis</i> Patton, 1905<p><small>Predicted distribution of <i>Anopheles (Cel.) arabiensis</i> Patton, 1905</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) arabiensis</i> Patton, 1905.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_arabiensisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_arabiensisExplorer__2010_Anopheles_arabiensisRectifiedGridCoverage-17.5416679382324 -30.291667938232463.5416679382324 21.0000019073486-17.541666666673166 -30.29166666666186863.54166666666082 21.00000000000521<i>Anopheles atroparvus</i> van Thiel, 1927<p><small>Predicted distribution of <i>Anopheles (Ano.) atroparvus</i> van Thiel, 1927</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) atroparvus</i> van Thiel, 1927.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_atroparvusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_atroparvusExplorer__2010_Anopheles_atroparvusRectifiedGridCoverage-9.50000095367432 32.416664123535241.75 58.1250038146973-9.500000000006821 32.4166666666689641.749999999993584 58.1250000000025<i>Anopheles balabacensis</i> Baisas, 1936<p><small>Predicted distribution of <i>Anopheles (Cel.) balabacensis</i> Baisas, 1936</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) balabacensis</i> Baisas, 1936.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_balabacensisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_balabacensisExplorer__2010_Anopheles_balabacensisRectifiedGridCoverage108.624992370605 -8.20833396911621121.29167175293 12.7083339691162108.62499999998846 -8.20833333332942121.29166666665523 12.70833333333741<i>Anopheles coluzzii</i><p><small>Predicted distribution of <i>Anopheles coluzzii</i></small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_coluzzii.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_coluzziiExplorer__2017_Anopheles_coluzzii.Mean_DecompressedRectifiedGridCoverage-20.000000000006395 -39.9999999999956659.99999999999361 40.00000000000435-20.0 -40.060.0 40.0<i>Anopheles culicifacies</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) culicifacies</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) culicifacies</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_culicifaciesWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_culicifaciesExplorer__2010_Anopheles_culicifaciesRectifiedGridCoverage40.4999961853027 5.91666650772095108.41667175293 35.166667938232440.49999999999116 5.916666666670025108.41666666665837 35.16666666667025<i>Anopheles darlingi</i> Root, 1926<p><small>Predicted distribution of <i>Anopheles (Nys.) darlingi</i> Root, 1926</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) darlingi</i> Root, 1926.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_darlingiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_darlingiExplorer__2010_Anopheles_darlingiRectifiedGridCoverage-93.2500076293945 -29.625-33.7916641235352 19.6666679382324-93.25000000000347 -29.624999999995225-33.79166666666966 19.66666666667183<i>Anopheles dirus</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) dirus</i> species complex</p></small><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) dirus</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_dirus_complexWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_dirus_complexExplorer__2010_Anopheles_dirus_complexRectifiedGridCoverage75.9999923706055 1.125122.0 27.958333969116275.99999999998977 1.1250000000035385121.99999999999014 27.958333333337084<i>Anopheles dirus</i><p><small>Predicted distribution of <i>Anopheles dirus</i></small></p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Anopheles_dirusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2016_Anopheles_dirusExplorer__2016_Anopheles_dirusRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Anopheles farauti</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) farauti</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) farauti</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_farautiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_farautiExplorer__2010_Anopheles_farautiRectifiedGridCoverage124.124992370605 -21.125170.25 2.66666674613953124.12499999998784 -21.124999999995566170.24999999998818 2.66666666667129<i>Anopheles flavirostris</i> (Ludlow, 1914)<p><small>Predicted distribution of <i>Anopheles (Cel.) flavirostris</i> (Ludlow, 1914)</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) flavirostris</i> (Ludlow, 1914).</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_flavirostrisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_flavirostrisExplorer__2010_Anopheles_flavirostrisRectifiedGridCoverage95.0416641235352 -11.0416669845581127.375 18.666667938232495.04166666665566 -11.041666666662636127.37499999998926 18.666666666670935<i>Anopheles fluviatilis</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) fluviatilis</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) fluviatilis</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_fluviatilisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_fluviatilisExplorer__2010_Anopheles_fluviatilisRectifiedGridCoverage47.6666641235352 8.0416660308837988.5 34.416667938232447.66666666665756 8.0416666666699488.49999999999122 34.41666666667015<i>Anopheles freeborni</i> Aitken, 1939<p><small>Predicted distribution of <i>Anopheles (Ano.) freeborni</i> Aitken, 1939</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) freeborni</i> Aitken, 1939.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_freeborniWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_freeborniExplorer__2010_Anopheles_freeborniRectifiedGridCoverage-124.750007629395 30.3333320617676-103.166664123535 51.9166679382324-124.75000000000222 30.333333333335702-103.1666666666687 51.91666666666921<i>Anopheles funestus</i> Giles, 1900<p><small>Predicted distribution of <i>Anopheles (Cel.) funestus</i> Giles, 1900</small></p><p><b>This is a predictive map showing the probability of occurrence for the <i>Anopheles (Cellia) funestus</i> Subgroup Giles, 1900.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_funestusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_funestusExplorer__2010_Anopheles_funestusRectifiedGridCoverage-17.5416679382324 -31.208333969116251.2083358764648 17.9583339691162-17.541666666673166 -31.20833333332849751.208333333327374 17.958333333338558<i>Anopheles funestus</i> Group Giles, 1900<p><small>Predicted distribution of <i>Anopheles (Cel.) funestus</i> Group Giles, 1900</small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_funestus_group.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_funestus_groupExplorer__2017_Anopheles_funestus_group.Mean_DecompressedRectifiedGridCoverage-20.000000000006395 -39.9999999999950559.999999999993605 40.000000000004945-20.0 -40.060.0 40.0<i>Anopheles funestus</i> Subgroup Giles, 1900<p><small>Predicted distribution of <i>Anopheles (Cel.) funestus</i> Subgroup Giles, 1900</small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_funestus_subgroup.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_funestus_subgroupExplorer__2017_Anopheles_funestus_subgroup.Mean_DecompressedRectifiedGridCoverage-20.000000000006395 -39.9999999999950559.999999999993605 40.000000000004945-20.0 -40.060.0 40.0<i>Anopheles funestus</i><p><small>Predicted distribution of <i>Anopheles (Cel.) funestus</i></small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anophele_funestus.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anophele_funestusExplorer__2017_Anophele_funestus.Mean_DecompressedRectifiedGridCoverage-20.000000000006395 -39.9999999999950559.999999999993605 40.000000000004945-20.0 -40.060.0 40.0<i>Anopheles gambiae</i> Giles, 1902<p><small>Predicted distribution of <i>Anopheles (Cel.) gambiae</i> Giles, 1902</small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_gambiae.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_gambiae_ssExplorer__2017_Anopheles_gambiae.Mean_DecompressedRectifiedGridCoverage-20.000000000006395 -39.9999999999951459.9999999999936 40.00000000000486-20.0 -40.060.0 40.0<i>Anopheles gambiae</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) gambiae</i> species complex</small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_gambiae_complex.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_gambiae_complexExplorer__2017_Anopheles_gambiae_complex.Mean_DecompressedRectifiedGridCoverage-20.000000000006498 -39.9999999999951159.99999999999351 40.0000000000049-20.0 -40.060.0 40.0<i>Anopheles gambiae</i><p><small>Predicted distribution of <i>Anopheles (Cel.) gambiae</i></small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) gambiae</i> Giles, 1902.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_gambiae_ssWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_gambiae_ssExplorer__2010_Anopheles_gambiae_ssRectifiedGridCoverage-17.5416679382324 -29.166667938232450.5 20.0416679382324-17.541666666673166 -29.1666666666619150.499999999994046 20.041666666671816<i>Anopheles koliensis</i> Owen, 1945<p><small>Predicted distribution of <i>Anopheles (Cel.) koliensis</i> Owen, 1945</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) koliensis</i> Owen, 1945.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_koliensisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_koliensisExplorer__2010_Anopheles_koliensisRectifiedGridCoverage130.08332824707 -11.0161.583343505859 -0.333333313465118130.08333333332092 -10.999999999995964161.58333333332118 -0.3333333333292128<i>Anopheles labranchiae</i> Falleroni, 1926<p><small>Predicted distribution of <i>Anopheles (Ano.) labranchiae</i> Falleroni, 1926</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) labranchiae</i> Falleroni, 1926.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_labranchiaeWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_labranchiaeExplorer__2010_Anopheles_labranchiaeRectifiedGridCoverage-6.12500047683716 33.37519.625 47.1666679382324-6.125000000006963 33.3750000000022619.624999999993236 47.16666666666903<i>Anopheles lesteri</i> Baisas & Hu, 1936<p><small>Predicted distribution of <i>Anopheles (Ano.) lesteri</i> Baisas & Hu, 1936</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) lesteri</i> Baisas & Hu, 1936 (formerly <i>An. anthropophagus</i> in China).</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_lesteriWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_lesteriExplorer__2010_Anopheles_lesteriRectifiedGridCoverage100.999992370605 4.58333301544189154.04167175293 43.2916679382324100.99999999998875 4.583333333336739154.04166666665583 43.29166666667039<i>Anopheles leucosphyrus</i> and <i>Anopheles latens</i><p><small>Predicted distribution of <i>Anopheles (Cel.) leucosphyrus</i> and <i>Anopheles (Cel.) latens</i></small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) leucosphyrus</i> and <i>Anopheles (Cellia) latens</i>.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_leuco_latensWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_leuco_latensExplorer__2010_Anopheles_leuco_latensRectifiedGridCoverage95.0416641235352 -5.95833349227905119.0 11.041666984558195.04166666665566 -5.958333333329506118.99999999998919 11.04166666667063<i>Anopheles leucosphyrus</i> complex<p><small>Predicted distribution of <i>Anopheles (Cel.) leucosphyrus</i> complex</small></p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Anopheles_leucosphyrus_complexWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2016_Anopheles_leucosphyrus_complexExplorer__2016_Anopheles_leucosphyrus_complexRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Anopheles leucosphyrus</i> group<p><small>Predicted distribution of <i>Anopheles (Cel.) leucosphyrus</i> group</small></p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Anopheles_leucosphyrus_groupWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2016_Anopheles_leucosphyrus_groupExplorer__2016_Anopheles_leucosphyrus_groupRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Anopheles maculatus</i> Group<p><small>Predicted distribution of <i>Anopheles (Cel.) maculatus</i> Group</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) maculatus</i> Group.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_maculatusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_maculatusExplorer__2010_Anopheles_maculatusRectifiedGridCoverage63.7083320617676 -11.0416669845581127.375 34.458335876464863.708333333323566 -11.041666666662636127.37499999999075 34.45833333333773<i>Anopheles marajoara</i> Galvão & Damasceno, 1942<p><small>Predicted distribution of <i>Anopheles (Nys.) marajoara</i> Galvão & Damasceno, 1942</small><p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) marajoara</i> Galvão & Damasceno, 1942.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_marajoaraWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_marajoaraExplorer__2010_Anopheles_marajoaraRectifiedGridCoverage-92.9583358764648 -25.0833339691162-32.3333320617676 17.9583339691162-92.95833333333681 -25.08333333332874-32.33333333333633 17.958333333338267<i>Anopheles melas</i> Theobald, 1903<p><small>Predicted distribution of <i>Anopheles (Cel.) melas</i> Theobald, 1903</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) melas</i> Theobald, 1903.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_melasWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_melasExplorer__2010_Anopheles_melasRectifiedGridCoverage-20.0000019073486 -36.055.0 40.0000038146973-20.000000000006395 -35.9999999999949754.9999999999942 40.00000000000563<i>Anopheles merus</i> Dönitz, 1902<p><small>Predicted distribution of <i>Anopheles (Cel.) merus</i> Dönitz, 1902</small></p><p>This raster (~5km) gives the mean modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>Full model details are given in the accompanying paper.</p> The following rasters are all improvements on previous versions published by MAP in 2011: <ul> <li>Anopheles arabiensis</li> <li>Anopheles funestus subgroup (the earlier publication called their map Anopheles funestus, but it was the subgroup)</li> <li>Anopheles melas</li> <li>Anopheles merus</li> </ul> <p>The rasters for Anopheles coluzzii and Anopheles gambiae together are an improvement on the previous Anopheles gambiae map.</p> <p>These rasters are freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Wiebe A, Longbottom J, Gleave K, Shearer FM, Massey NC, Bhatt S, Cameron E, Sinka ME, Smith DL, Hemingway J, Gething PW, Colman M, Moyes CL. Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance. Malaria Journal, 16: 85.</p>2017_Anopheles_merus.Mean_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2017_Anopheles_merusExplorer__2017_Anopheles_merus.Mean_DecompressedRectifiedGridCoverage-20.000000000008207 -39.9999999999950859.999999999991815 40.000000000004924-20.0 -40.060.0 40.0<i>Anopheles merus</i> Dönitz, 1902<p><small>Predicted distribution of <i>Anopheles (Cel.) merus</i> Dönitz, 1902</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) merus</i> Dönitz, 1902.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_merusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(archive)rPackage:2010_Anopheles_merusExplorer__2010_Anopheles_merusRectifiedGridCoverage27.9166660308838 -30.458333969116263.5416679382324 1.2083333730697627.916666666658358 -30.45833333332852563.541666666658635 1.2083333333383912<i>Anopheles messeae</i> Falleroni, 1926<p><small>Predicted distribution of <i>Anopheles (Ano.) messeae</i> Falleroni, 1926</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) messeae</i> Falleroni, 1926.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_messeaeWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_messeaeExplorer__2010_Anopheles_messeaeRectifiedGridCoverage-10.6250009536743 40.6666641235352124.333335876465 66.5000076293945-10.625000000006793 40.66666666666862124.3333333333276 66.50000000000216<i>Anopheles minimus</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) minimus</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) minimus</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_minimusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_minimusExplorer__2010_Anopheles_minimusRectifiedGridCoverage84.3749923706055 0.416666656732559122.04167175293 29.500001907348684.37499999998943 0.4166666666702383122.0416666666564 29.5000000000038<i>Anopheles moucheti</i> Evans, 1925<p><small>Predicted distribution of <i>Anopheles (Cel.) moucheti</i> Evans, 1925</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) moucheti</i> Evans, 1925.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_mouchetiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_mouchetiExplorer__2010_Anopheles_mouchetiRectifiedGridCoverage2.70833325386047 -12.833333969116234.5 9.291666984558112.70833333332601 -12.83333333332923634.49999999999293 9.291666666670938<i>Anopheles nili</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) nili</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) nili</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_niliWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_niliExplorer__2010_Anopheles_niliRectifiedGridCoverage-17.2083339691162 -30.546.875 15.7500009536743-17.20833333333985 -30.4999999999951946.874999999993975 15.75000000000518<i>Anopheles nuneztovari</i> species complex<p><small>Predicted distribution of <i>Anopheles (Nys.) nuneztovari</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Nyssorhynchus) nuneztovari</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_nuneztovariWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_nuneztovariExplorer__2010_Anopheles_nuneztovariRectifiedGridCoverage-77.7500076293945 -24.25-28.8333320617676 11.2500009536743-77.75000000000409 -24.24999999999544-28.833333333337038 11.250000000004846<i>Anopheles pseudopuncti</i> species complex<p><small>Predicted distribution of <i>Anopheles (Ano.) pseudopunctipennis</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) pseudopunctipennis</i>.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_pseudopunctiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_pseudopunctiExplorer__2010_Anopheles_pseudopunctiRectifiedGridCoverage-109.458335876465 -28.125-60.5 39.7083358764648-109.45833333333616 -28.12499999999529-60.50000000000244 39.708333333338594<i>Anopheles punctulatus</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) punctulatus</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) punctulatus</i>.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_punctulatusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_punctulatusExplorer__2010_Anopheles_punctulatusRectifiedGridCoverage124.124992370605 -11.6666669845581160.91667175293 2.66666674613953124.12499999998784 -11.666666666662607160.9166666666548 2.6666666666708405<i>Anopheles quadrimacul</i> Say, 1824<p><small>Predicted distribution of <i>Anopheles (Ano.) quadrimaculatus</i> Say, 1824</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) quadrimaculatus</i> Say, 1824.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_quadrimaculWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_quadrimaculExplorer__2010_Anopheles_quadrimaculRectifiedGridCoverage-102.875007629395 24.5-68.2916641235352 48.5416679382324-102.87500000000308 24.500000000002615-68.29166666666947 48.54166666666947<i>Anopheles sacharovi</i> Favre, 1903<p><small>Predicted distribution of <i>Anopheles (Ano.) sacharovi</i> Favre, 1903</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) sacharovi</i> Favre, 1903.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_sacharoviWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_sacharoviExplorer__2010_Anopheles_sacharoviRectifiedGridCoverage8.12499904632568 28.291666030883854.4166679382324 47.83333587646488.124999999992468 28.2916666666691354.4166666666595 47.83333333333595<i>Anopheles sergentii</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) sergentii</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) sergentii species</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_sergentiiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_sergentiiExplorer__2010_Anopheles_sergentiiRectifiedGridCoverage-18.2083339691162 12.583333015441971.125 37.3750038146973-18.208333333339823 12.58333333333641271.12499999999422 37.375000000003276<i>Anopheles sinensis</i> species complex<p><small>Predicted distribution of <i>Anopheles (Ano.) sinensis</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Anopheles) sinensis</i>.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_sinensisWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_sinensisExplorer__2010_Anopheles_sinensisRectifiedGridCoverage69.2499923706055 -6.0154.0 50.916667938232469.24999999999002 -5.999999999996177153.99999999999068 50.91666666667094<i>Anopheles stephensi</i> Grassi, 1899<p><small>Predicted distribution of <i>Anopheles (Cel.) stephensi</i> Liston, 1901</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) stephensi</i> Grassi, 1899.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_stephensiWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_stephensiExplorer__2010_Anopheles_stephensiRectifiedGridCoverage43.4583320617676 8.04166603088379102.54167175293 34.708335876464843.45833333332439 8.04166666666994102.5416666666582 34.70833333333682<i>Anopheles subpictus</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) subpictus</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) subpictus</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_subpictusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_subpictusExplorer__2010_Anopheles_subpictusRectifiedGridCoverage69.1249923706055 -11.6666669845581156.0 35.750003814697369.12499999999002 -11.666666666662607155.99999999999073 35.750000000004434<i>Anopheles sundaicus</i> species complex<p><small>Predicted distribution of <i>Anopheles (Cel.) sundaicus</i> species complex</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) sundaicus</i> species complex.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_sundaicusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_sundaicusExplorer__2010_Anopheles_sundaicusRectifiedGridCoverage81.5416641235352 -12.25134.04167175293 26.291667938232481.54166666665623 -12.249999999995921134.04166666665662 26.29166666667105<i>Anopheles superpictus</i> Grassi, 1899<p><small>Predicted distribution of <i>Anopheles (Cel.) superpictus</i> Grassi, 1899</small></p><p><b>This is a predictive map showing the probability of occurrence for <i>Anopheles (Cellia) superpictus</i> Grassi, 1899.</b></p> <p>Red indicates where our model predicts that the probability of finding the mosquito species is high and blue areas are where the model predicts that the probability of finding the species is low. Regional versions of this map also display species occurrence data as reported in the published literature (the black dots).</p> <p>These predictions were generated using the Boosted Regression Tree modelling methodology which also produced a ranked list of environmental variables assessed to be influential in predicting the presence of this species. These environmental variables are given in the paper below.</p> <p><i>Note: The mosquito species occurrence data were collated with the aim of providing a global perspective, and the predictive maps we have produced are based on a model output across a large, regional scale. Whilst remaining informative, it is important to note that some individual country maps may not be able to represent all of the fine scale variation that exists in the mosquito distribution.</i></p>2010_Anopheles_superpictusWCSImageMosaicrastercategory:surfacessubcategory:vector_occurrencesubsubcategory:vector_occurrence_(current)rPackage:2010_Anopheles_superpictusExplorer__2010_Anopheles_superpictusRectifiedGridCoverage9.74999904632568 27.791666030883880.8333358764648 49.33333587646489.749999999992411 27.79166666666914480.83333333332631 49.333333333335986<i>Macaca fascicularis</i><p><small>Predicted distribution of <i>Macaca fascicularis</i></small><p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Macaca_fascicularisWCSImageMosaicrastercategory:surfacessubcategory:zoonotic_malariasubsubcategory:hostsrPackage:2016_Macaca_fascicularisExplorer__2016_Macaca_fascicularisRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Macaca leonina</i><p><small>Predicted distribution of <i>Macaca leonina</i></small><p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Macaca_leoninaWCSImageMosaicrastercategory:surfacessubcategory:zoonotic_malariasubsubcategory:hostsrPackage:2016_Macaca_leoninaExplorer__2016_Macaca_leoninaRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Macaca nemestrina</i><p><small>Predicted distribution of <i>Macaca nemestrina</i></small><p><p>Each one of these rasters (~5km) is the modelled relative probability of occurrence for that species, complex or group, on a scale 0 to 1.0.</p> <p>The raster extent is defined by the knowlesi study area.</p> <p>Model outputs were masked out (pixels assigned a value of 0) on islands that the species in question does not inhabit (based on ranges defined in 2015).</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Moyes CL, Shearer FM, Huang Z, Wiebe A, Gibson HS, Nijman V, Mohd-Azlan J, Brodie JF, Malaivijitnond S, Linkie M, Samejima H, O’Brien TG, Trainor CR, Hamada Y, Giordano AJ, Kinnaird MF, Elyazar IRF, Sinka ME, Vythilingam I, Bangs MJ, Pigott DM, Weiss DJ, Golding N and Hay SI (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of the Plasmodium knowlesi malaria parasite in forested and non-forested areas. Parasite & Vectors, 9: 242.</p>2016_Macaca_nemestrinaWCSImageMosaicrastercategory:surfacessubcategory:zoonotic_malariasubsubcategory:hostsrPackage:2016_Macaca_nemestrinaExplorer__2016_Macaca_nemestrinaRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Plasmodium falciparum</i> Incidence.<p><small><i>Plasmodium falciparum</i> incidence rate globally, 2000-2017</small></p><p>This layer is a time-aware mosaic data set showing predicted all-age incidence rate (clinical cases per 1,000 population per annum) of <i>Plasmodium falciparum</i> malaria for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between rates of zero and 10 cases per 1000 (grey shades) and a second linear scale between 10 and 1000 (colours from purple to yellow).</p> <p>Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_Pf_IncidenceWCSImageMosaiccategory:surfacessubcategory:malaria_risktemplaterastersubsubcategory:clinical_burdenrPackage:2019_Global_Pf_IncidenceExplorer__2019_Global_Pf_IncidenceRectifiedGridCoverage-179.99997535 -60.0000005764648179.999889288477 84.9999467265625-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> Incidence version 2020<p><small><i>Plasmodium falciparum</i> incidence rate globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic data set showing predicted all-age incidence rate (clinical cases per 1,000 population per annum) of <i>Plasmodium falciparum</i> malaria for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between rates of zero and 10 cases per 1000 (grey shades) and a second linear scale between 10 and 1000 (colours from purple to yellow).</p> <p>Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_Pf_IncidenceWCSImageMosaiccategory:surfacessubcategory:malaria_risktemplaterastersubsubcategory:clinical_burdenrPackage:2020_Global_Pf_Incidenceversion:betaExplorer__2020_Global_Pf_IncidenceRectifiedGridCoverage-180.00000762939504 -60.00000254313147180.000015258789 85.00000127156576-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> Incidence<p><small><i>Plasmodium falciparum</i> incidence rate in Africa, 2000-2015</small></p><p><b>This layer is a time-aware mosaic data set showing the modelled parasite prevalence rate of <i>Plasmodium falciparum</i> malaria for each year.</b></p> <p>A study by the Malaria Atlas Project presented an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantified the effects of the interventions that have been implemented to combat the disease. The authors found the prevalence of <i>Plasmodium falciparum</i> infection had been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.</p> <p>The layer shows the number of cases of the disease, per person per year.</p>2015_Nature_Africa_IncidenceWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:clinical_burdenrPackage:2015_Incidence_AfricaExplorer__2015_Nature_Africa_IncidenceRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648 -35.0000152.04157385 37.54162765<i>Plasmodium falciparum</i> Mortality Rate version 2020<p><small><i>Plasmodium falciparum</i> mortality rate per 100k people globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic dataset showing the predicted all-age <i>Plasmodium falciparum</i> mortality rate (deaths per 100,000 population per annum) for each year.</p> <p>Although this layer is visualised here as deaths per 100k, note that the underlying data (should you download them) reflect deaths per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_Pf_Mortality_RateWCSImageMosaicrastercategory:surfacessubcategory:malaria_risktemplatesubsubcategory:clinical_burdenrPackage:2020_Global_Pf_Mortality_Rateversion:betaExplorer__2020_Global_Pf_Mortality_RateRectifiedGridCoverage-180.00000762939504 -60.00000254313147180.000015258789 85.00000127156576-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> Mortality Rate<p><small><i>Plasmodium falciparum</i> mortality rate per 100k people globally, 2000-2017</small></p><p>This layer is a time-aware mosaic dataset showing the predicted all-age <i>Plasmodium falciparum</i> mortality rate (deaths per 100,000 population per annum) for each year.</p> <p>Although this layer is visualised here as deaths per 100k, note that the underlying data (should you download them) reflect deaths per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_Pf_Mortality_RateWCSImageMosaicrastercategory:surfacessubcategory:malaria_risktemplatesubsubcategory:clinical_burdenrPackage:2019_Global_Pf_Mortality_RateExplorer__2019_Global_Pf_Mortality_RateRectifiedGridCoverage-179.99997535 -60.0000005764648179.999889288477 84.9999467265625-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> PR_{2 - 10} version 2020<p><small><i>Plasmodium falciparum</i> parasite rate in 2-10 year olds globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic data set showing predicted age-standardised parasite rate for <i>Plasmodium falciparum</i> malaria for children two to ten years of age (<i>Pf</i>PR_2-10) for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between zero and 0.01 <i>Pf</i>PR_2-10 (grey shades) and a second linear scale between 0.01 and 1 (colours from blue to red).</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_PfPRWCSImageMosaiccategory:surfacessubcategory:malaria_risksubsubcategory:endemicitytemplaterasterrPackage:2020_Global_PfPRversion:betaExplorer__2020_Global_PfPRRectifiedGridCoverage-180.00000762939504 -60.00000254313147180.000015258789 85.00000127156576-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> PR_{2 - 10}<p><small><i>Plasmodium falciparum</i> parasite rate in 2-10 year olds globally, 2000-2017</small></p><p>This layer is a time-aware mosaic data set showing predicted age-standardised parasite rate for <i>Plasmodium falciparum</i> malaria for children two to ten years of age (<i>Pf</i>PR_2-10) for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between zero and 0.01 <i>Pf</i>PR_2-10 (grey shades) and a second linear scale between 0.01 and 1 (colours from blue to red).</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_PfPRWCSImageMosaiccategory:surfacessubcategory:malaria_risksubsubcategory:endemicitytemplaterasterrPackage:2019_Global_PfPRExplorer__2019_Global_PfPRRectifiedGridCoverage-179.99997535 -60.0000005764648179.999889288477 84.9999467265625-180.0 -59.999999999994180.0 85.0000000000061<i>Plasmodium falciparum</i> PR_2-10<p><small><i>Plasmodium falciparum</i> parasite rate in 2-10 year olds in Africa, 2000-2015</small></p><p><b>This layer is a time-aware mosaic data set showing the modelled parasite prevalence rate of <i>Plasmodium falciparum</i> malaria for each year.</b></p> <p>A study by the Malaria Atlas Project presented an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantified the effects of the interventions that have been implemented to combat the disease. The authors found the prevalence of <i>Plasmodium falciparum</i> infection had been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.</p> <p>Units are prevalence rate as a proportion of all children aged 2-10.</p>2015_Nature_Africa_PRWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:endemicityrPackage:2015_PR_AfricaExplorer__2015_Nature_Africa_PRRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648 -35.0000152.04157385 37.54162765<i>Plasmodium falciparum</i> Reproductive Number<p><small>Spatial distribution of <i>P. falciparum</i> reproductive number under control, 2019</p></small><p><b>This layer shows point estimates of basic reproductive number under control (P<i>f</i>R_c) within the limits of stable <i>Plasmodium falciparum</i> transmission, illustrating the potential for the disease to spread within a naive population moderated by malaria control.</b></p> <p>The estimates of P<i>f</i>R_c were generated using a malaria transmission model to describe the relationship between P<i>f</i>R_c and the predicted probability distribution of parasite prevalence (P<i>f</i>PR_2-10). This means that the estimates of P<i>f</i>R_c encapsulate uncertainty in both the underlying prevalence estimates and in the parameterisation of the malaria transmission model. The model generated multiple estimates at each location (a posterior distribution) and this map show the median estimate at each location (approx. 1km²).</p>2020_Global_Pf_Reproductive_NumberWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:reproductive_numberrPackage:2020_Global_Pf_Reproductive_NumberExplorer__2020_Global_Pf_Reproductive_NumberRectifiedGridCoverage-118.375 -55.08336236466009170.2499000000002 53.5416-118.375 -55.08336236466009170.2499000000002 53.5416<i>Plasmodium falciparum</i> Spatial Limits<p><small>Spatial limits of <i>Plasmodium falciparum</i> malaria transmission globally, 2010</small></p><p><b>This layer shows the estimated limits of <i>Plasmodium falciparum</i> malaria transmission.</b></p> <p>Areas shown in light grey are those likely to be risk-free. This classification arises because either health system surveillance data (annual case incidence) reported zero cases for three consecutive years, because low temperatures or high aridity are likely to preclude transmission, or because specific medical intelligence exists for the area defining it as risk free.</p> <p>Areas shown in light pink are those at risk of unstable malaria transmission. These areas are those where local transmission cannot be ruled out, but levels of risk are extremely low, with annual case incidence reported at less than 1 per 10,000.</p> <p>Areas in red are those at risk of stable malaria transmission. This is a very broad classification of risk including any regions where the annual case incidence is likely to exceed 1 per 10,000. Within this category we carry out more elaborate spatial modelling based on parasite rate data and environmental variables, providing maps of endemicity.</p> <p>We use annual case incidence data over the most recent four years (where we have access to the data) and at the smallest district size available.</p>2010_Pf_Limits_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:transmission_limitsrPackage:2010_Pf_LimitsExplorer__2010_Pf_Limits_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium falciparum</i> Support<p><small>Days per year which could support infectious <i>P. falciparum</i> vectors, 2010 </small><p><p><b>This layer shows the number of days per year which could support infectious vectors, calculated using a dynamic biological model and spatial time series temperature data.</b></p> <p>The temperature data used was a continuous time series across an average year (1950-2000) for every approx. 1km².</p> <p>The biological model incorporates the impact of temperature on 1) vector lifespan and 2) the duration of <i>Plasmodium falciparum</i> sporogeny. If the temperature at a site never allows mosquitoes to survive long enough to support sporogeny then the area is unsuitable and is shaded grey.</p>2010_TempSuitability.Pf.AnnualInfectiousDays.1k.global_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:temperature_suitabilityrPackage:2010_Temperature_Suitability_PfExplorer__2010_TempSuitability.Pf.AnnualInfectiousDays.1k.global_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium falciparum</i> Temperature Suitability<p><small>Temperature suitability index for <i>P. falciparum</i> transmission, 2010</small></p><p><b>This layer shows the temperature suitability for <i>Plasmodium falciparum</i> transmission globally, calculated using a dynamic biological model and spatial time series temperature data.</b></p> <p>The temperature data used was a time series across an average year (1950-2000) for every approx. 1km².</p> <p>The biological model incorporates the impact of temperature on 1) vector lifespan and 2) the duration of <i>P. falciparum</i> sporogeny. The index of suitability is a relative, not absolute, measure of the effect of temperature on vectorial capacity and reproductive number. All other things being equal, it is <b>a relative measure of the number of infectious mosquitoes in an environment defined by the temperature profile at that location, given a constant adult mosquito recruitment rate</b>. Index values of 0 indicate that no vectors survive long enough to support transmission and the environment is therefore unsuitable all year round (shaded grey on the map).</p>2010_TempSuitability.Pf.Index.1k.global_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:temperature_suitabilityrPackage:2010_Temperature_Suitability_Pf_IndexExplorer__2010_TempSuitability.Pf.Index.1k.global_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium knowlesi</i> Risk<p><small>Modelled relative risk of zoonotic <i>Plasmodium knowlesi</i>, 2016</p></small><p>This raster (~5km) is the modelled relative risk of zoonotic <i>Plasmodium knowlesi</i> infecting humans on a scale 0 (low) to 1.0 (high).</p> <p>The raster extent is defined by the <i>knowlesi</i> study area.</p> <p>Pixels that do not have at least one reservoir host species and at least one vector species (as defined by our 2016 distributions for these species), have been adjusted to 0.</p> <p>Full model details are given in the accompanying paper.</p> <p>This raster is freely available to all under the terms of the Creative Commons Attribution 4.0 International License, and the attribution is: Shearer FM, Huang Z, Weiss DJ, Wiebe A, Gibson HS, Battle KE, Pigott DM, Brady OJ, Putaporntip C, Jongwutiwes S, Lau YL, Manske M, Amato R, Elyazar IRF, Vythilingam I, Bhatt S, Gething PW, Singh B, Golding N, Hay SI and Moyes CL (2016) Estimating geographical variation in the risk of zoonotic <i>Plasmodium knowlesi</i> infection in countries eliminating malaria. PLoS Neglected Tropical Diseases, 10: e0004915.</p>2016_Pk_SEAsia_maskedWCSImageMosaicrastercategory:surfacessubcategory:zoonotic_malariasubsubcategory:malaria_riskrPackage:2016_Pk_Risk_SEAsiaExplorer__2016_Pk_SEAsia_maskedRectifiedGridCoverage84.4999999999966 -14.999999999999396164.083333333327 33.874999999998684.4999999999966 -14.999999999999396164.083333333327 33.8749999999986<i>Plasmodium vivax</i> Cases</sub> version 2020<p><small><i>Plasmodium vivax</i> incidence count in all ages globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic data set showing the predicted all-age incidence count (clinical cases) of <i>Plasmodium vivax</i> malaria for each year. </p> <p>The numbers of cases predicted to occur in each 5x5km pixel are shown on a spectrum of blue to red. Areas where <i>P. vivax</i> is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_Pv_CasesWCSImageMosaicrastercategory:surfacessubcategory:malaria_risktemplatesubsubcategory:clinical_burdenExplorer__2020_Global_Pv_CasesRectifiedGridCoverage-118.375007629395 -55.0833358764648170.250015258789 53.5416679382324-118.375007629395 -55.0833358764648170.250015258789 53.5416679382324<i>Plasmodium vivax</i> Cases</sub><p><small><i>Plasmodium vivax</i> incidence count in all ages globally, 2000-2017</small></p><p>This layer is a time-aware mosaic data set showing the predicted all-age incidence count (clinical cases) of <i>Plasmodium vivax</i> malaria for each year. </p> <p>The numbers of cases predicted to occur in each 5x5km pixel are shown on a spectrum of blue to red. Areas where <i>P. vivax</i> is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_Pv_CasesWCSImageMosaicrastercategory:surfacessubcategory:malaria_risktemplatesubsubcategory:clinical_burdenExplorer__2019_Global_Pv_CasesRectifiedGridCoverage-118.375 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324<i>Plasmodium vivax</i> Incidence version 2020<p><small><i>Plasmodium vivax</i> incidence rate globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic data set showing the predicted all-age incidence rate (clinical cases per 1,000 population per annum) of <i>Plasmodium vivax</i> for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between rates of zero and one (grey shades) and a second linear scale between >1 case per 1,000 to >600 cases per 1,000 (colours from blue to red).</p> <p>Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_Pv_IncidenceWCSImageMosaicrastertemplatecategory:surfacessubcategory:malaria_risksubsubcategory:clinical_burdenrPackage:2020_Global_Pv_Incidenceversion:betaExplorer__2020_Global_Pv_IncidenceRectifiedGridCoverage-118.375007629395 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.083333333170.250000001374 53.541666667517<i>Plasmodium vivax</i> Incidence<p><small><i>Plasmodium vivax</i> incidence rate globally, 2000-2017</small></p><p>This layer is a time-aware mosaic data set showing the predicted all-age incidence rate (clinical cases per 1,000 population per annum) of <i>Plasmodium vivax</i> for each year.</p> <p>Note the colour scaling is split to better differentiate within low endemic areas, with one linear scale between rates of zero and one (grey shades) and a second linear scale between >1 case per 1,000 to >600 cases per 1,000 (colours from blue to red).</p> <p>Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_Pv_IncidenceWCSImageMosaicrastertemplatecategory:surfacessubcategory:malaria_risksubsubcategory:clinical_burdenrPackage:2019_Global_Pv_IncidenceExplorer__2019_Global_Pv_IncidenceRectifiedGridCoverage-118.375 -55.0833358764648170.249893188477 53.5416259765625-118.375 -55.083333333170.250000001374 53.541666667517<i>Plasmodium vivax</i> PR_1-99 version 2020<p><small><i>Plasmodium vivax</i> parasite rate in all ages globally, 2000-2019.</small></p><b><p>MAP tries to publish the most up-to-date version of our results. Please contact us if you have questions related to versioning.</p></b> <p>This layer is a time-aware mosaic data set showing the predicted all-age parasite prevalence rate for <i>Plasmodium vivax</i> malaria for each year.</p> <p>The prevalence predicted to occur in each 5x5km pixel are shown on a spectrum of light blue to red. Areas where <i>P. vivax</i> is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2020_Global_PvPRWCSImageMosaicrastercategory:surfacessubsubcategory:endemicitysubcategory:malaria_risktemplaterPackage:2020_Global_PvPRversion:betaExplorer__2020_Global_PvPRRectifiedGridCoverage-118.375007629395 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.083333333170.250000001612 53.5416666676067<i>Plasmodium vivax</i> PR_1-99<p><small><i>Plasmodium vivax</i> parasite rate in all ages globally, 2000-2017</small></p><p>This layer is a time-aware mosaic data set showing the predicted all-age parasite prevalence rate for <i>Plasmodium vivax</i> malaria for each year.</p> <p>The prevalence predicted to occur in each 5x5km pixel are shown on a spectrum of light blue to red. Areas where <i>P. vivax</i> is known to be endemic, but there was not sufficient information to generate a prediction are shown in light grey.</p> <p>Please see our <a href="/malaria-burden">malaria burden</a> page for further details.</p>2019_Global_PvPRWCSImageMosaicrastercategory:surfacessubsubcategory:endemicitysubcategory:malaria_risktemplaterPackage:2019_Global_PvPRExplorer__2019_Global_PvPRRectifiedGridCoverage-118.375 -55.0833358764648170.249893188477 53.5416259765625-118.375 -55.083333333170.250000001612 53.5416666676067<i>Plasmodium vivax</i> Relapse<p><small><i>Plasmodium vivax</i> relapse incidence per 100,000 person days globally, 2013</p></small><p><b>This map shows the predicted <i>Plasmodium vivax</i> relapse.</b></p> <p>The incidence rate of relapse was calculated from number of first relapse events to occur in 30,049 records of P. vivax cases along with the total person time observed. The factors affecting incidence were modelled using a mixed-effects meta-analysis in R.</p> <p>Various geographic zones to describe variation in relapse periodicity were included in the meta-analysis of relapse rate. The boundaries shown in the map represent the zones that produced the best model, which were defined by similar climatic, malaria transmission and vector conditions.</p> <p>Areas outside the epidemiological zones are shown in grey. Relapse incidence is shown on a spectrum of blue to red, with the highest relapse incidence in red. In some regions, the modelled rate varied from raw calculated rates due to low numbers of relapse observations. The zone that includes India was particularly uncertain because the prediction due to few relapse observations relative to the large number of case records and is hatched in the map. Highest relapse incidence is predicted to occur in Southeast Asia and Melanesia and South America.</p>2013_Pv_Relapse_Incidence_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:clinical_burdenrPackage:2013_Pv_Relapse_IncidenceExplorer__2013_Pv_Relapse_Incidence_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium vivax</i> Spatial Limits<p><small>Spatial limits of <i>Plasmodium vivax</i> malaria transmission globally, 2010</small></p><p><b>This layer shows the estimated limits of <i>Plasmodium vivax</i> malaria transmission.</b></p> <p>Areas shown in grey are those likely to be risk-free. This classification arises because either surveillance data (annual case incidence) reported zero cases for three consecutive years, because low temperatures or high aridity are likely to preclude transmission, or because specific medical intelligence exists for the area defining it as risk free.</p> <p>Areas shown in light pink are those with unstable malaria transmission. These areas are those where local transmission cannot be ruled out, but levels are extremely low, with annual case incidence reported at less than 1 per 10,000.</p> <p>Areas in red are those with stable malaria transmission. This is a very broad classification of risk including any regions where the annual case incidence is likely to exceed 1 per 10,000. Within this category we carry out more elaborate spatial modelling based on parasite rate data and environmental variables, providing maps of <i>P. vivax</i> endemicity.</p> <p>We use annual case incidence data over the most recent four years (where we have access to the data) and at the smallest district size available.</p>2010_Pv_limits_5k_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:transmission_limitsrPackage:2010_Pv_LimitsExplorer__2010_Pv_limits_5k_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium vivax</i> Support<p><small>Days per year which could support infectious <i>P. vivax</i> vectors, 2010 </small><p><p><b>This layer shows the number of days per year which could support infectious vectors, calculated using a dynamic biological model and spatial time series temperature data.</b></p> <p>The temperature data used was a continuous time series across an average year (1950-2000) for every approx. 1km².</p> <p>The biological model incorporates the impact of temperature on 1) vector lifespan and 2) the duration of <i>Plasmodium vivax</i> sporogeny. If the temperature at a site never allows mosquitoes to survive long enough to support sporogeny then the area is unsuitable and is shaded grey.</p>2010_TempSuitability.Pv.AnnualInfectiousDays.1k.global_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:temperature_suitabilityrPackage:2010_Temperature_Suitability_PvExplorer__2010_TempSuitability.Pv.AnnualInfectiousDays.1k.global_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0<i>Plasmodium vivax</i> Temperature Suitability<p><small>Temperature suitability index for <i>P. vivax</i> transmission, 2010</small></p><p><b>This layer shows the temperature suitability for <i>Plasmodium vivax</i> transmission globally, calculated using a dynamic biological model and spatial time series temperature data.</b></p> <p>The temperature data used was a time series across an average year (1950-2000) for every approx. 1km².</p> <p>The biological model incorporates the impact of temperature on 1) vector lifespan and 2) the duration of <i>P. vivax sporogeny</i>. The index of suitability is a relative, not absolute, measure of the effect of temperature on vectorial capacity and reproductive number. All other factors being equal, it is <b>a relative measure of the number of infectious mosquitoes in an environment defined by the temperature profile at that location, given a constant adult mosquito recruitment rate</b>. Index values of 0 indicate that no vectors survive long enough to support transmission and the environment is therefore unsuitable all year round (shaded grey on the map).</p>2010_TempSuitability.Pv.Index.1k.global_DecompressedWCSGeoTIFFrastercategory:surfacessubcategory:malaria_risksubsubcategory:temperature_suitabilityrPackage:2010_Temperature_Suitability_Pv_IndexExplorer__2010_TempSuitability.Pv.Index.1k.global_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0A global friction surface enumerating land-based travel speed for a nominal year 2015<p><small>Minutes required to travel one metre</p><p>This global friction surface enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2015. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce this "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel, with the fastest travel mode intersecting the pixel being used to determine the speed of travel in that pixel (with some exceptions such as national boundaries, which have the effect of imposing a travel time penalty). This map represents the travel speed from this allocation process, expressed in units of minutes required to travel one metre. It forms the underlying dataset behind the global accessibility map described in the referenced paper.</p> <p>Shows the estimated land-based travel speed across each pixel, for every land pixel between 85 degrees north and 60 degrees south, at a 30 arcsecond resolution. This is called a "friction surface" and underlies the accessibility dataset described in "Mapping inequality in accessibility: a global assessment of travel time to cities in 2015" (Weiss et al 2018; doi:10.1038/nature25181). The speed units are expressed as minutes required to travel one metre.</p> <p>Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate this global friction surface of average land-based travel speed. Each dataset and class was allocated a speed of travel using the process described in the accompanying paper. At each pixel the intersecting data type with the fastest travel speed was used to determine the pixel value, with exceptions such as national borders which took priority and served to reduce travel speed. Please see the associated publication for full details of the processing.</p>2015_friction_surface_v1_DecompressedWCSrastercategory:surfacessubcategory:human_populationsubsubcategory:accessibility_(2015)rPackage:2015_Friction_SurfaceImageMosaicExplorer__2015_friction_surface_v1_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0A global map of travel time to cities to assess inequalities in accessibility in 2015<p><small>Predicted travel time (minutes) to nearest city</small></p><p>This is a predictive map showing the estimated time to travel from every point on earth to the nearest (in time) city.</p> <p><a href="http://www.openstreetmap.org/copyright">Contains data from OpenStreetMap</a> © OpenStreetMap contributors.</p>2015_accessibility_to_cities_v1.0WCSrastercategory:surfacessubcategory:human_populationsubsubcategory:accessibility_(2015)rPackage:2015_Accessibility_To_CitiesGeoTIFFExplorer__2015_accessibility_to_cities_v1.0RectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0All-cause fever<p><small>Predicted all-cause fever prevalence within limits of stable <i>Pf</i> transmission in children under 5 years of age, 2006-2014</small></p><p><b>Abstract</b></p> <p>Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both malaria-attributable and non-malarial fever in sub-Saharan African children from 2006-2014. We estimate that 34.2% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 31.1% of those (10.6% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.</p> <p>This layer shows predicted all-cause fever prevalence within limits of stable <i>Pf</i> transmission in children under 5 years of age.</p>2017_All_Cause_FeverWCSImageMosaicrastercategory:surfacessubcategory:febrile_illnesssubsubcategory:malaria_attributionrPackage:2017_All_Cause_FeverExplorer__2017_All_Cause_FeverRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-17.9999999999993 -34.999999999998652.0416666666646 37.5416666666652Artemisinin-based combination therapy (ACT) coverage<p><small>ACT coverage in Africa, 2000-2015</small></p><p><b>This layer shows the coverage of artemisinin-based combination therapy for the treatment of fever symptoms, for the years 2000-2015 for all African countries where P<i>f</i> malaria is endemic.</b></p> <p>A study by the Malaria Atlas Project presented an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantified the effects of the interventions that have been implemented to combat the disease. The authors found the prevalence of <i>Plasmodium falciparum</i> infection had been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.</p> <p>Units show the percentage of cases of fever in under-5 year olds that were treated with ACT.</p>2015_Nature_Africa_ACTWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2015_ACT_AfricaExplorer__2015_Nature_Africa_ACTRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648 -35.0000152.04157385 37.54162765Dominant Vectors<p><small>Dominant malaria vector species globally, 2010</small></p><p><b>This layer shows the set of mosquito species that have been assessed as the most important for malaria transmission in each malaria endemic area.</b></p> <p>We used the predictive maps for each species to generate this multiple vector species map and only those areas with a presence probability greater than 0.5 were included. Our Technical Advisory Group (TAG) identified the three most important vector species per country (where there were three or more species) and ranked these species by their relative importance and the top-ranked species were displayed preferentially. In those areas where the dominance of one species was not clear, the predictive maps for each dominant species were merged.</p> <p><b>Notes on the Asia Pacific</b></p> <p>Due to the complexity of mosquito species found in the Asia Pacific region, all vector species found in each country within the region were ranked by importance. Additionally, where the importance of a vector species is known to vary greatly across a country's geographical extent, more detailed species-specific spatial information was gathered.</p> <p><b>Notes on Africa</b></p> <p>For Africa, the layer illustrates the distribution of those species considered to be of major importance. There are other important vector species so a second map of Africa dedicated to showing the distributions of these 'secondary' dominant vector species is also provided.</p>2010_Dominant_Vector_Species_Global_5kWCSImageMosaictemplaterastercategory:surfacessubcategory:vector_occurrencesubsubcategory:multiple_vectorsrPackage:2010_Dominant_Vector_SpeciesExplorer__2010_Dominant_Vector_Species_Global_5kRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Duffy Negativity Phenotype FrequencyThis layer map provides estimates of the proportion of population predicted to be Duffy negative stratified into 12 classes of prevalence. Duffy negative individuals tend to be resistant to Plasmodium vivax. The frequency of Duffy negativity in the population, therefore, modulates estimates of the population at risk of Plasmodium vivax. Data from community surveys of both the Duffy genotype and phenotype were used in a Bayesian geostatistical model to generate predictions of the phenotype prevalence. The model generates a range of estimates at each location (a posterior distribution) and this layer shows the median estimate for each posterior distribution.201201_Global_Duffy_Negativity_Phenotype_FrequencyWCSGeoTIFFspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2010Blood_Disorders__201201_Global_Duffy_Negativity_Phenotype_FrequencyRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Duffy Negativity Phenotype Frequency<p><small>The spatial distribution of the Duffy negative phenotype globally, 2010</p></small><p><b>This layer shows the proportion of the population predicted to be Duffy negative stratified into 12 classes of prevalence.</b></p> <p>We are interested in Duffy negativity because Duffy negative individuals tend to be resistant to <i>Plasmodium vivax</i> malaria. The frequency of Duffy negativity in the population, therefore, modulates our estimates of the population at risk of <i>Plasmodium vivax</i> malaria.</p> <p>Data from community surveys of both the Duffy genotype and phenotype were used in a Bayesian geostatistical model to generate predictions of the phenotype prevalence. The model generates a range of estimates at each location (a posterior distribution) and this layer shows the median estimate for each posterior distribution. This approach allows us to assess the uncertainty in our predictions.</p>2011_Duffy_Negativity_Phenotype_Frequency_5k_DecompressedWCSGeoTIFFrastercategory:surfacessubsubcategory:duffy_negativitysubcategory:blood_disordersrPackage:2011_Duffy_Negativity_Phenotype_FrequencyExplorer__2011_Duffy_Negativity_Phenotype_Frequency_5k_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Effective treatment with an Antimalarial drug version 2020<p><small>Effective treatment with an Antimalarial drug globally, 2000-2019. This is a beta version posted in advance of forthcoming publications.</small></p><b><p>This is a beta version posted in advance of forthcoming publications.</p></b> <p>This layer shows the spatiotemporal distribution of effective treatment with an Antimalarial drug for the years 2000-2019 for all countries where malaria is endemic.</p>2020_Global_AM_Effective_TreatmentWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2020_Global_AM_Effective_TreatmentExplorer__2020_Global_AM_Effective_TreatmentRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.25 53.5416679382324G6PD Deficiency Allele Frequency<p><small>Median predicted allele frequency for G6PD deficiency globally</p></small><p><b>This layer shows the predicted allele frequency for G6PD deficiency within malaria endemic countries.</b></p> <p>Estimates were made based on data from community surveys which fed into a geostatistical model configured to give gene frequency estimates for populations on a spatial scale and adapted to reflect the X-linked inheritance of the G6PD gene. The model output a range of estimates (a posterior distribution) at each location (~5x5km) and this layer shows the median value.</p>2012_G6PDd_Allele_Frequency_Global_5k_DecompressedWCSGeoTIFFrastercategory:surfacessubsubcategory:G6PD_deficiencysubcategory:blood_disordersrPackage:2012_G6PDd_Allele_FrequencyExplorer__2012_G6PDd_Allele_Frequency_Global_5k_DecompressedRectifiedGridCoverage-120.999999999995 -56.99999999999771170.04166666666003 56.0416666666644-120.999999999995 -56.99999999999771170.04166666666003 56.0416666666644Global friction surface enumerating land-based travel speed with access to motorized transportThis layer map provides estimates of time required to traverse each pixel within an approximately one-by one-kilometer gridded representation of the Earth’s surface. The speed units are expressed as minutes required to travel one metre. The movement rates encapsulated within the friction surfaces were based on landscape characteristics and transportation infrastructure, and no individual movement data was used to create them. The friction surfaces were made by combining geographic datasets that each characterize a layer-specific cost of moving through the landscape. These datasets consisted of roads, railways, navigable waterways, bodies of water, land cover types, topographic conditions (elevation and slope angle), and national borders. Descriptions of these datasets and their associated processing steps are provided in Weiss et al. (2018). Note, however, that we did not consider air travel in this analysis. The road layers used to derive the friction surfaces were the most critical inputs for overall model accuracy. The roads datasets used for this project were the OSM and Google Roads datasets. OSM roads data were extracted in October 2019 and converted into a rasterized surface of road types whereby the fastest road type in each pixel took precedence. In a second processing step, the OSM data were analyzed to determine the median speed limit value for each road type in each country (extended data 1). These values were subsequently used to attribute road speeds to specific pixels using a lookup table. In cases where a road type was present in a country, but associated speed limit information was unavailable, a global average speed limit for that road type was used instead. The Google roads data consisted of a distance to road layer, which effectively became a rasterized roads surface by defining all pixels with distance values of less than 500 meters as roads. Because the OSM data were split by road type for which we had speed limit information, these roads data took precedence when both datasets indicated the existence of roads within a pixel. In cases where only Google roads data indicated the presence of a road, those pixels were assigned the generic “road” classification for purposes of assigning speed limits via the lookup tables. While the approach for making the friction surface was identical to the one used in Weiss et al. (2018), reanalysis was warranted because substantial additions have since been made to OSM. These changes included the addition of many previously missing roads, particularly in developing countries; and far more comprehensive speed limit information to support country-specific road speed attribution202001_Global_Motorized_Friction_SurfaceWCSGeoTIFFspatialExtent:GlobalspatialResolution:1KmtemporalExtent:2019Accessibility__202001_Global_Motorized_Friction_SurfaceRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Global friction surface enumerating land-based travel speed with access to motorized transport for a nominal year 2019<p><small>Minutes required to travel one metre</p><p>This friction surface quantifies the time required to traverse each pixel within an approximately one-by one-kilometer gridded representation of the Earth’s surface. The movement rates encapsulated within the friction surfaces were based on landscape characteristics and transportation infrastructure, and no individual movement data was used to create them. The friction surfaces were made by combining geographic datasets that each characterize a layer-specific cost of moving through the landscape. These datasets consisted of roads, railways, navigable waterways, bodies of water, land cover types, topographic conditions (elevation and slope angle), and national borders. Descriptions of these datasets and their associated processing steps are provided in Weiss et al. (2018). Note, however, that we did not consider air travel in this analysis.</p> <p>The road layers used to derive the friction surfaces were the most critical inputs for overall model accuracy. The roads datasets used for this project were the OSM and Google Roads datasets. OSM roads data were extracted in October 2019 and converted into a rasterized surface of road types whereby the fastest road type in each pixel took precedence. In a second processing step, the OSM data were analyzed to determine the median speed limit value for each road type in each country (extended data 1). These values were subsequently used to attribute road speeds to specific pixels using a lookup table. In cases where a road type was present in a country, but associated speed limit information was unavailable, a global average speed limit for that road type was used instead. The Google roads data consisted of a distance to road layer, which effectively became a rasterized roads surface by defining all pixels with distance values of less than 500 meters as roads. Because the OSM data were split by road type for which we had speed limit information, these roads data took precedence when both datasets indicated the existence of roads within a pixel. In cases where only Google roads data indicated the presence of a road, those pixels were assigned the generic “road” classification for purposes of assigning speed limits via the lookup tables. While the approach for making the friction surface was identical to the one used in Weiss et al. (2018), reanalysis was warranted because substantial additions have since been made to OSM. These changes included the addition of many previously missing roads, particularly in developing countries; and far more comprehensive speed limit information to support country-specific road speed attribution.</p>2020_motorized_friction_surfaceWCSGeoTIFFrastercategory:surfacessubcategory:human_populationrPackage:2020_Motorized_Friction_Surfacesubsubcategory:accessibility_(2019)Explorer__2020_motorized_friction_surfaceRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Global friction surface enumerating land-based travel walking-only speed without access to motorized transportThis layer map provides estimates of time required to traverse each pixel within an approximately one-by one-kilometer gridded representation of the Earth’s surface. The speed units are expressed as minutes required to travel one metre. The movement rates encapsulated within the friction surfaces were based on landscape characteristics and transportation infrastructure, and no individual movement data was used to create them. The friction surfaces were made by combining geographic datasets that each characterize a layer-specific cost of moving through the landscape. These datasets consisted of roads, railways, navigable waterways, bodies of water, land cover types, topographic conditions (elevation and slope angle), and national borders. Descriptions of these datasets and their associated processing steps are provided in Weiss et al. (2018). Note, however, that we did not consider air travel in this analysis. In this walking-only friction surface, we assumed that all road travel was limited to a walking speed of five kilometers per hour and all water travel was reduced to the rate for traversing a wetland or swimming of 1.0 kilometer per hour. The walking-only friction provided an upper bound of movement rates for individuals who lack access to, or cannot use, motorized transport202001_Global_Walking_Only_Friction_SurfaceWCSGeoTIFFspatialResolution:1KmspatialExtent:GlobaltemporalExtent:2019Accessibility__202001_Global_Walking_Only_Friction_SurfaceRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Global friction surface enumerating land-based travel walking-only speed without access to motorized transport for a nominal year 2019<p><small>Minutes required to travel one metre</p><p>This friction surface quantifies the time required to traverse each pixel within an approximately one-by one-kilometer gridded representation of the Earth’s surface. The movement rates encapsulated within the friction surfaces were based on landscape characteristics and transportation infrastructure, and no individual movement data was used to create them. The friction surfaces were made by combining geographic datasets that each characterize a layer-specific cost of moving through the landscape. These datasets consisted of roads, railways, navigable waterways, bodies of water, land cover types, topographic conditions (elevation and slope angle), and national borders. Descriptions of these datasets and their associated processing steps are provided in Weiss et al. (2018). Note, however, that we did not consider air travel in this analysis.</p> <p>In this walking-only friction surface, we assumed that all road travel was limited to a walking speed of five kilometers per hour and all water travel was reduced to the rate for traversing a wetland or swimming of 1.0 kilometer per hour. The walking-only friction provided an upper bound of movement rates for individuals who lack access to, or cannot use, motorized transport.</p>2020_walking_only_friction_surfaceWCSGeoTIFFrastercategory:surfacessubcategory:human_populationrPackage:2020_Walking_Only_Friction_Surfacesubsubcategory:accessibility_(2019)Explorer__2020_walking_only_friction_surfaceRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Global travel time to healthcare map with access to motorized transport<p><small>Estimated travel time (minutes) to nearest healthcare facility</small></p><p>This is a map showing the estimated time to travel from every point on earth to the nearest healthcare facility by motorized transport. Travel times were calculated to the nearest geolocated hospital or clinic. A critical assumption of the travel time calculations, however, was that people moved through the landscape at an optimal speed. In the optimal friction surface, we assumed that humans traveling along roadways, railways, and on water moved at the motorized movement rates assigned to each type.</p> <p>We utilized healthcare facility locations from two of the largest global databases: (1) OpenStreetMap (OSM) data that was collated and made available for download at www.healthsites.io29; and (2) data from Google Maps that was extracted specifically for this project. We augmented the global datasets with continental-scale facility locations that were recently published for Africa (Maina, J. et al., 2019) and Australia (Barbieri, S. & Jorm, 2019). To align temporally, each of these datasets was extracted in mid-2019. To facilitate comparisons between data sources, we used only facilities defined as hospitals and clinics, which matched the definition used in Maina, J. et al. (2019) and Barbieri, S. & Jorm (2019), and was distinguishable within the OSM data by using the facility-type attribute. In contrast, Google Maps data were filtered to isolate hospitals and clinics from the full set of geolocated entities. For each dataset, multiple points found within the same approximately one-by-one-kilometer pixel were merged to match the resolution of the subsequent travel time maps.</p>2020_motorized_travel_time_to_healthcareWCSGeoTIFFrastercategory:surfacessubcategory:human_populationrPackage:2020_Motorized_Travel_Time_To_Healthcaresubsubcategory:accessibility_(2019)Explorer__2020_motorized_travel_time_to_healthcareRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Glucose-6-Phosphate Dehydrogenase (G6PD) deficiency frequencyThis layer map provides estimates of predicted allele frequency for G6PD deficiency. These estimates were produced based on data from community surveys which fed into a geostatistical model configured to give gene frequency estimates for populations on a spatial scale and adapted to reflect the X-linked inheritance of the G6PD gene. The model output a range of estimates (a posterior distribution) at each location (~5x5km) and this layer shows the median value.201201_Global_G6PDd_Allele_FrequencyWCSGeoTIFFspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2010Blood_Disorders__201201_Global_G6PDd_Allele_FrequencyRectifiedGridCoverage-121.0 -57.0170.04167175293 56.0416679382324-120.999999999995 -56.99999999999771170.04166666666003 56.0416666666644Haemoglobin C (HbC) Allele FrequencyThis layer map provides estimates of predicted allele frequency for Haemoglobin C in Africa. Mean point estimates of allele frequency predicted using a Bayesian geostatistical model. The model output a range of estimates (a posterior distribution) at each location (~5x5km) and this layer shows the median value.201201_Africa_HbC_Allele_FrequencyWCStemporalExtent:2010spatialExtent:AfricaspatialResolution:5KmBlood_Disorders__201201_Africa_HbC_Allele_FrequencyRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-18.0 -35.052.04166666666666 37.54166666666666HbC (Haemoglobin C) Allele Frequency<p><small>Predicted allele frequency in Africa</p></small><p><b>This layer shows the predicted allele frequency for Haemoglobin C in Africa.</b></p> <p>Mean point estimates of allele frequency predicted using a Bayesian geostatistical model. For further information, please see our <a href="https://map.ox.ac.uk/research-project/blood-disorders/#HbC">research page on this topic</a></p>2013_HbC_Allele_Freq_Africa_5kWCSGeoTIFFrastercategory:surfacessubsubcategory:haemoglobin_Csubcategory:blood_disordersrPackage:2013_HbC_Allele_Frequency_AfricaExplorer__2013_HbC_Allele_Freq_Africa_5kRectifiedGridCoverage-18.0 -35.052.04166666666666 37.54166666666666-18.0 -35.052.04166666666666 37.54166666666666HbS (Sickle Haemoglobin) Allele Frequency<p><small>Mean estimates of sickle haemoglobin allele frequency globally, 2010</p></small><p><b>This layer shows the predicted frequency of sickle haemoglobin alleles in the general population.</b></p> <p>Estimates were based on data from community surveys which fed into a geostatistical model configured to generate gene frequency estimates. The model output a range of estimates (a posterior distribution) at each location (~5x5km) and the mean estimate for each location is shown on the above map. The interquartile range of estimates at each location is shown in the map below as a measure of uncertainty in the predictions displayed above.</p>2013_Sickle_Haemoglobin_HbS_Allele_Freq_Global_5k_DecompressedWCSGeoTIFFrastercategory:surfacessubsubcategory:sickle_cellsubcategory:blood_disordersrPackage:2013_HbS_Allele_FrequencyExplorer__2013_Sickle_Haemoglobin_HbS_Allele_Freq_Global_5k_DecompressedRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Indoor Residual Spraying (IRS) coverage version 2020<p><small>IRS coverage in Africa, 2000-2017. This is a beta version posted in advance of forthcoming publications.</small></p><b><p>This is a beta version posted in advance of forthcoming publications.</p></b> <p>This layer shows the spatiotemporal distribution of Indoor Residual Spraying (IRS) coverage for the years 2000-2017 for all African countries where malaria is endemic.</p>2020_Africa_IRS_CoverageWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2020_Africa_IRS_Coverageversion:betaExplorer__2020_Africa_IRS_CoverageRectifiedGridCoverage-17.5416679382324 -34.833335876464851.4166679382324 27.3333339691162-17.5416679382324 -34.833335876464851.4166679382324 27.3333339691162Indoor residual spraying (IRS) coverage<p><small>IRS coverage in Africa, 2000-2015</p></small><p><b>This map shows the percentage of the population protected by indoor residual spraying of insecticides.</b></p> <p>A study by the Malaria Atlas Project presented an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantified the effects of the interventions that have been implemented to combat the disease. The authors found the prevalence of <i>Plasmodium falciparum</i> infection had been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.</p> <p>Units are the percentage of the population protected by indoor residual spraying of insecticides.</p>2015_Nature_Africa_IRSWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2015_IRS_AfricaExplorer__2015_Nature_Africa_IRSRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648 -35.0000152.04157385 37.54162765Insecticide treated bednet (ITN) use version 2020<p><small>ITN use in Africa, 2000-2019. This is a beta version posted in advance of forthcoming publications.</small></p><b><p>This is a beta version posted in advance of forthcoming publications.</p></b> <p>This layer shows the spatiotemporal distribution of Insecticide treated bednet (ITN) use for the years 2000-2019 for all African countries where malaria is endemic.</p>2020_Africa_ITN_UseWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2020_Africa_ITN_Useversion:betatemplateExplorer__2020_Africa_ITN_UseRectifiedGridCoverage-18.0000019073486 -35.000003814697352.0416679382324 37.5416679382324-18.0000019073486 -35.000003814697352.0416679382324 37.5416679382324Insecticide-treated bednet (ITN) coverage<p><small>ITN coverage in Africa, 2000-2015</p></small><p><b>This layer is a time-aware image service showing the modelled coverage of insecticide-treated bednets for each year.</b></p> <p>A study by the Malaria Atlas Project presented an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantified the effects of the interventions that have been implemented to combat the disease. The authors found the prevalence of <i>Plasmodium falciparum</i> infection had been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.</p> <p>Units are proportion of the population who were protected by ITNs.</p>2015_Nature_Africa_ITNWCSImageMosaicrastercategory:surfacessubcategory:malaria_risksubsubcategory:interventionrPackage:2015_ITN_AfricaExplorer__2015_Nature_Africa_ITNRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648 -35.0000152.04157385 37.54162765Land-based Travel Speed Friction SurfaceThis layer map provides friction surface enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2015. The speed units are expressed as minutes required to travel one metre. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce this "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel, with the fastest travel mode intersecting the pixel being used to determine the speed of travel in that pixel (with some exceptions such as national boundaries, which have the effect of imposing a travel time penalty). This map represents the travel speed from this allocation process, expressed in units of minutes required to travel one metre. It forms the underlying dataset behind the global accessibility map described in the referenced paper.</p> Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate this global friction surface of average land-based travel speed. Each dataset and class was allocated a speed of travel using the process described in the accompanying paper. At each pixel the intersecting data type with the fastest travel speed was used to determine the pixel value, with exceptions such as national borders which took priority and served to reduce travel speed. The travel speed from this allocation process, expressed in units of minutes required to travel one metre.201501_Global_Travel_Speed_Friction_SurfaceWCSGeoTIFFspatialExtent:GlobalspatialResolution:1KmtemporalExtent:2015Accessibility__201501_Global_Travel_Speed_Friction_SurfaceRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Malaria-attributable fever as a proportion of all-cause fever<p><small>Predicted malaria attributable fevers (MAF) as a proportion of all fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission, 2006-2014</small></p><p><b>Abstract</b></p> <p>Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both malaria-attributable and non-malarial fever in sub-Saharan African children from 2006-2014. We estimate that 34.2% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 31.1% of those (10.6% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.</p> <p>This layer shows predicted malaria attributable fevers (MAF) as a proportion of all fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission.</p>2017_Malaria_Attributable_Prop_All_FeverWCSImageMosaicrastercategory:surfacessubcategory:febrile_illnesssubsubcategory:malaria_attributionrPackage:2017_Malaria_Attributable_Prop_All_FeverExplorer__2017_Malaria_Attributable_Prop_All_FeverRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-17.9999999999993 -34.999999999998652.0416666666646 37.5416666666652Malaria-attributable fever as a proportion of malaria-positive fever<p><small>Predicted malaria attributable fevers as a proportion of malaria positive fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission, 2006-2014</small></p><p><b>Abstract</b></p> <p>Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both malaria-attributable and non-malarial fever in sub-Saharan African children from 2006-2014. We estimate that 34.2% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 31.1% of those (10.6% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.</p> <p>This layer shows predicted malaria attributable fevers as a proportion of malaria positive fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission.</p>2017_Malaria_Attributable_Prop_Malaria_PositiveWCSImageMosaicrastercategory:surfacessubcategory:febrile_illnesssubsubcategory:malaria_attributionrPackage:2017_Malaria_Attributable_Prop_Malaria_PositiveExplorer__2017_Malaria_Attributable_Prop_Malaria_PositiveRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-17.9999999999993 -34.999999999998652.0416666666646 37.5416666666652Malaria-positive fever as a proportion of all-cause fever<p><small>Predicted malaria-positive fevers as a proportion of all fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission, 2006-2014</small></p><p><b>Abstract</b></p> <p>Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both malaria-attributable and non-malarial fever in sub-Saharan African children from 2006-2014. We estimate that 34.2% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 31.1% of those (10.6% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.</p> <p>This layer shows predicted malaria-positive fevers as a proportion of all fevers, for children under 5 years of age and bounded by the limits of stable <i>Pf</i> transmission.</p>2017_Malaria_Positive_Prop_All_FeverWCSImageMosaicrastercategory:surfacessubcategory:febrile_illnesssubsubcategory:malaria_attributionrPackage:2017_Malaria_Positive_Prop_All_FeverExplorer__2017_Malaria_Positive_Prop_All_FeverRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-17.9999999999993 -34.999999999998652.0416666666646 37.5416666666652Non-malarial fever<p><small>Non-malarial fever prevalence for children under 5 years of age and bounded within the spatial limits of stable <i>Pf</i> transmission, 2006-2014</small></p><p><b>Abstract</b></p> <p>Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both malaria-attributable and non-malarial fever in sub-Saharan African children from 2006-2014. We estimate that 34.2% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 31.1% of those (10.6% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.</p> <p>Non-malarial fever prevalence is defined as the sum of the prevalence of febrile illness without a <i>P. falciparum</i> malaria infection and the prevalence of febrile illness coincident with, but not caused by, a <i>P. falciparum</i> malaria infection, for children under 5 years of age and bounded within the spatial limits of stable <i>Pf</i> transmission.</p>2017_Non_Malarial_FeverWCSImageMosaicrastercategory:surfacessubcategory:febrile_illnesssubsubcategory:malaria_attributionrPackage:2017_Non_Malarial_FeverExplorer__2017_Non_Malarial_FeverRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-17.9999999999993 -34.999999999998652.0416666666646 37.5416666666652Number of deaths from Plasmodium falciparum during a defined year 2000-2020This layer map provides estimates of deaths from Plasmodium falciparum during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202206_Global_Pf_Mortality_CountWCSImageMosaictemplaterasterspatialResolution:5kspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2020Malaria__202206_Global_Pf_Mortality_CountRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -59.999999999994180.0 85.0000000000061Number of deaths from Plasmodium falciparum during a defined year 2000-2022This layer map provides estimates of deaths from Plasmodium falciparum during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202406_Global_Pf_Mortality_CountWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2022spatialResolution:5KmMalaria__202406_Global_Pf_Mortality_CountRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -59.999999999994180.0 85.0000000000061Number of deaths from Plasmodium falciparum during a defined year 2000-2024This layer map provides estimates of deaths from Plasmodium falciparum during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202508_Global_Pf_Mortality_CountWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2024spatialResolution:5KmMalaria__202508_Global_Pf_Mortality_CountRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of deaths from Plasmodium falciparum per 100,000 population during a defined year 2000-2020This layer map provides estimates of deaths from Plasmodium falciparum per 100,000 population during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. Although this layer is visualised here as deaths per 100k, note that the underlying data (should you download them) reflect deaths per person.202206_Global_Pf_Mortality_RateWCSImageMosaictemplaterasterspatialResolution:5kspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2020Malaria__202206_Global_Pf_Mortality_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -59.999999999994180.0 85.0000000000061Number of deaths from Plasmodium falciparum per 100,000 population during a defined year 2000-2022This layer map provides estimates of deaths from Plasmodium falciparum per 100,000 population during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of ≤5 people per pixel.202406_Global_Pf_Mortality_RateWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2022spatialResolution:5KmMalaria__202406_Global_Pf_Mortality_RateRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -59.999999999994180.0 85.0000000000061Number of deaths from Plasmodium falciparum per 100,000 population during a defined year 2000-2024This layer map provides estimates of deaths from Plasmodium falciparum per 100,000 population during a defined year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of >5 people per pixel.202508_Global_Pf_Mortality_RateWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalResolution:AnnualtemporalExtent:2000-2024spatialResolution:5KmMalaria__202508_Global_Pf_Mortality_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year 2000-2020This layer map provides estimates of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.202206_Global_Pf_Incidence_RateWCSImageMosaicspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2000-2020temporalResolution:AnnualMalaria__202206_Global_Pf_Incidence_RateRectifiedGridCoverage-118.375 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Number of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year 2000-2022This layer map provides estimates of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of ≤5 people per pixel.202406_Global_Pf_Incidence_RateWCSImageMosaicspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2000-2022temporalResolution:AnnualMalaria__202406_Global_Pf_Incidence_RateRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Number of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year 2000-2024This layer map provides estimates of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of >5 people per pixel.202508_Global_Pf_Incidence_RateWCSImageMosaicspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2000-2024temporalResolution:AnnualMalaria__202508_Global_Pf_Incidence_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year 2000-2025This layer map provides estimates of newly diagnosed Plasmodium falciparum cases per 1,000 population, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of >5 people per pixel.202608_Global_Pf_Incidence_RateWCSImageMosaicspatialResolution:5KmspatialExtent:GlobaltemporalExtent:2000-2025temporalResolution:AnnualMalaria__202608_Global_Pf_Incidence_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium falciparum cases, on a given year 2000-2020This layer map provides estimates of newly diagnosed Plasmodium falciparum cases, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202206_Global_Pf_Incidence_CountWCSImageMosaictemporalExtent:2000-2020temporalResolution:AnnualspatialExtent:GlobalspatialResolution:5KmMalaria__202206_Global_Pf_Incidence_CountRectifiedGridCoverage-118.375 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Number of newly diagnosed Plasmodium falciparum cases, on a given year 2000-2022This layer map provides estimates of newly diagnosed Plasmodium falciparum cases, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202406_Global_Pf_Incidence_CountWCSImageMosaictemporalExtent:2000-2022temporalResolution:AnnualspatialExtent:GlobalspatialResolution:5KmMalaria__202406_Global_Pf_Incidence_CountRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Number of newly diagnosed Plasmodium falciparum cases, on a given year 2000-2024This layer map provides estimates of newly diagnosed Plasmodium falciparum cases, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202508_Global_Pf_Incidence_CountWCSImageMosaictemporalExtent:2000-2024temporalResolution:AnnualspatialExtent:GlobalspatialResolution:5KmMalaria__202508_Global_Pf_Incidence_CountRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium falciparum cases, on a given year 2000-2025This layer map provides estimates of newly diagnosed Plasmodium falciparum cases, on a given year. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202608_Global_Pf_Incidence_CountWCSImageMosaictemporalExtent:2000-2025temporalResolution:AnnualspatialExtent:GlobalspatialResolution:5KmMalaria__202608_Global_Pf_Incidence_CountRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year 2000-2020This layer map provides estimates of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. Although this layer is visualised here as cases per 1k, note that the underlying data (should you download them) reflect cases per person.202206_Global_Pv_Incidence_RateWCSImageMosaictemplaterasterspatialResolution:5kspatialExtent:GlobaltemporalExtent:2000-2020temporalResolution:AnnualMalaria__202206_Global_Pv_Incidence_RateRectifiedGridCoverage-180.00000000000006 -60.00004450480147180.000015258789 84.99995930989586-180.0 -59.999999999994180.0 85.0000000000061Number of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year 2000-2022This layer map provides estimates of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202406_Global_Pv_Incidence_RateWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2022temporalResolution:AnnualspatialResolution:5KmMalaria__202406_Global_Pv_Incidence_RateRectifiedGridCoverage-180.00000000000006 -60.00004450480147180.000015258789 84.99995930989586-180.0 -59.999999999994180.0 85.0000000000061Number of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year 2000-2024This layer map provides estimates of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202508_Global_Pv_Incidence_RateWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2024temporalResolution:AnnualspatialResolution:5KmMalaria__202508_Global_Pv_Incidence_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year 2000-2025This layer map provides estimates of newly diagnosed Plasmodium vivax cases per 1,000 population, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202608_Global_Pv_Incidence_RateWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2025temporalResolution:AnnualspatialResolution:5KmMalaria__202608_Global_Pv_Incidence_RateRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium vivax cases, on a given year 2000-2020This layer map provides estimates of newly diagnosed Plasmodium vivax cases, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time.202206_Global_Pv_Incidence_CountWCSImageMosaictemplaterasterspatialResolution:5kspatialExtent:GlobaltemporalExtent:2000-2020temporalResolution:AnnualMalaria__202206_Global_Pv_Incidence_CountRectifiedGridCoverage-180.00000000000006 -60.00004450480147180.000015258789 84.99995930989586-180.0 -59.999999999994180.0 85.0000000000061Number of newly diagnosed Plasmodium vivax cases, on a given year 2000-2022This layer map provides estimates of newly diagnosed Plasmodium vivax cases, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as counts. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202406_Global_Pv_Incidence_CountWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2022temporalResolution:AnnualspatialResolution:5KmMalaria__202406_Global_Pv_Incidence_CountRectifiedGridCoverage-180.00000000000006 -60.00004450480147180.000015258789 84.99995930989586-180.0 -59.999999999994180.0 85.0000000000061Number of newly diagnosed Plasmodium vivax cases, on a given year 2000-2024This layer map provides estimates of newly diagnosed Plasmodium vivax cases, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as counts. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202508_Global_Pv_Incidence_CountWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2024temporalResolution:AnnualspatialResolution:5KmMalaria__202508_Global_Pv_Incidence_CountRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Number of newly diagnosed Plasmodium vivax cases, on a given year 2000-2025This layer map provides estimates of newly diagnosed Plasmodium vivax cases, on a given year. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as counts. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202608_Global_Pv_Incidence_CountWCSImageMosaictemplaterasterspatialExtent:GlobaltemporalExtent:2000-2025temporalResolution:AnnualspatialResolution:5KmMalaria__202608_Global_Pv_Incidence_CountRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0000038146973180.000015258789 85.0Prevalence of improved housing 2000<p><small>in sub-Saharan Africa, in 2000</small></p>This predictive surface shows the prevalence of improved housing in 2000, predicted at 5 x 5-km2 resolution. For further details see https://map.ox.ac.uk/research-project/housing_in_africa/2019_Nature_Africa_Housing_2000WCSGeoTIFFrastercategory:surfacessubcategory:human_populationsubsubcategory:housingrPackage:2019_Africa_Housing_2000Explorer__2019_Nature_Africa_Housing_2000RectifiedGridCoverage-17.54173165 -34.8750100500000151.41657409999999 37.4166277-17.54173165 -34.8750100500000151.41657409999999 37.4166277Prevalence of improved housing 2015<p><small>in sub-Saharan Africa, in 2015</small></p>This predictive surface shows the prevalence of improved housing in 2015, predicted at 5 x 5-km2 resolution. For further details see https://map.ox.ac.uk/research-project/housing_in_africa/2019_Nature_Africa_Housing_2015WCSGeoTIFFrastercategory:surfacessubcategory:human_populationsubsubcategory:housingrPackage:2019_Africa_Housing_2015Explorer__2019_Nature_Africa_Housing_2015RectifiedGridCoverage-17.54173165 -34.8750100500000151.41657409999999 37.4166277-17.54173165 -34.8750100500000151.41657409999999 37.4166277Proportion of Children 2 to 10 years of age showing, on a given year, detectable Plasmodium falciparum parasite 2000-2020This layer map provides estimates of the proportion of children 2 to 10 years of age (PfPR2-10) showing detectable Plasmodium falciparum parasite. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution.202206_Global_Pf_Parasite_RateWCSImageMosaictemporalResolution:AnnualtemporalExtent:2000-2020spatialExtent:GlobalspatialResolution:5KmMalaria__202206_Global_Pf_Parasite_RateRectifiedGridCoverage-118.375 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Proportion of Children 2 to 10 years of age showing, on a given year, detectable Plasmodium falciparum parasite 2000-2022This layer map provides estimates of the proportion of children 2 to 10 years of age (PfPR2-10) showing detectable Plasmodium falciparum parasite. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of ≤5 people per pixel.202406_Global_Pf_Parasite_RateWCSImageMosaictemporalResolution:AnnualtemporalExtent:2000-2022spatialExtent:GlobalspatialResolution:5KmconfidenceReport:Malaria:202406_Global_Pf_Parasite_Rate_ConfidenceMalaria__202406_Global_Pf_Parasite_RateRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Proportion of Children 2 to 10 years of age showing, on a given year, detectable Plasmodium falciparum parasite 2000-2024This layer map provides estimates of the proportion of children 2 to 10 years of age (PfPR2-10) showing detectable Plasmodium falciparum parasite. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of >5 people per pixel.202508_Global_Pf_Parasite_RateWCSImageMosaictemporalResolution:AnnualtemporalExtent:2000-2024spatialExtent:GlobalspatialResolution:5KmconfidenceReport:Malaria:202508_Global_Pf_Parasite_Rate_ConfidenceMalaria__202508_Global_Pf_Parasite_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Proportion of Children 2 to 10 years of age showing, on a given year, detectable Plasmodium falciparum parasite 2000-2025This layer map provides estimates of the proportion of children 2 to 10 years of age (PfPR2-10) showing detectable Plasmodium falciparum parasite. To produce these estimates, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The maps cover all malaria-endemic countries and are produced annually from year 2000 at a 5x5 km resolution. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and 1; the 1 values represent pixels that we mask out because they are sparsely populated. This masking is only applied to South America, and we define "sparsely populated" as areas that have a modelled estimate of >0 and a population of >5 people per pixel.202608_Global_Pf_Parasite_RateWCSImageMosaictemporalResolution:AnnualtemporalExtent:2000-2025spatialExtent:GlobalspatialResolution:5KmconfidenceReport:Malaria:202608_Global_Pf_Parasite_Rate_ConfidenceMalaria__202608_Global_Pf_Parasite_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Proportion of Malaria Cases that Receive Effective Treatment with an Antimalarial Medicine 2000-2020Spatiotemporal distribution of effective treatment with an antimalarial drug202106_Global_Antimalarial_Effective_TreatmentWCSImageMosaictemporalExtent:2000-2020temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalInterventions__202106_Global_Antimalarial_Effective_TreatmentRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.25 53.5416679382324Proportion of Malaria Cases that Receive Effective Treatment with an Antimalarial Medicine 2000-2022Spatiotemporal distribution of effective treatment with an antimalarial drug202406_Global_Antimalarial_Effective_TreatmentWCSImageMosaictemporalExtent:2000-2022temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalInterventions__202406_Global_Antimalarial_Effective_TreatmentRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.25 53.5416679382324Proportion of Malaria Cases that Receive Effective Treatment with an Antimalarial Medicine 2000-2024Spatiotemporal distribution of effective treatment with an antimalarial drug202508_Global_Antimalarial_Effective_TreatmentWCSImageMosaictemporalExtent:2000-2024temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalInterventions__202508_Global_Antimalarial_Effective_TreatmentRectifiedGridCoverage-118.375 -55.0833358764648170.250015258789 53.5416679382324-118.375 -55.0833358764648170.250015258789 53.5416679382324Proportion of Malaria Cases that Receive Effective Treatment with an Antimalarial Medicine 2000-2025Spatiotemporal distribution of effective treatment with an antimalarial drug202608_Global_Antimalarial_Effective_TreatmentWCSImageMosaictemporalExtent:2000-2025temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalInterventions__202608_Global_Antimalarial_Effective_TreatmentRectifiedGridCoverage-118.375 -55.083333333333336170.25 53.541674296061174-118.375 -55.0833358764648170.250015258789 53.5416679382324Proportion of People Sleeping under an Insecticide-Treated Net, among those with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2020This layer map provides estimates of the proportion of people sleeping under an Insecticide-Treated Net, among those with access to an Insecticide-Treated Net in their household. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202106_Africa_Insecticide_Treated_Net_Use_RateWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2020Interventions__202106_Africa_Insecticide_Treated_Net_Use_RateRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of People Sleeping under an Insecticide-Treated Net, among those with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2022This layer map provides estimates of the proportion of people sleeping under an Insecticide-Treated Net, among those with access to an Insecticide-Treated Net in their household. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202406_Africa_Insecticide_Treated_Net_Use_RateWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2022Interventions__202406_Africa_Insecticide_Treated_Net_Use_RateRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of People Sleeping under an Insecticide-Treated Net, among those with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2024This layer map provides estimates of the proportion of people sleeping under an Insecticide-Treated Net, among those with access to an Insecticide-Treated Net in their household. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202508_Africa_Insecticide_Treated_Net_Use_RateWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2024Interventions__202508_Africa_Insecticide_Treated_Net_Use_RateRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Proportion of People Sleeping under an Insecticide-Treated Net, among those with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2025This layer map provides estimates of the proportion of people sleeping under an Insecticide-Treated Net, among those with access to an Insecticide-Treated Net in their household. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202608_Africa_Insecticide_Treated_Net_Use_RateWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2025Interventions__202608_Africa_Insecticide_Treated_Net_Use_RateRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Proportion of Population with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2020This layer map provides estimates of the proportion of population with access to an Insecticide-Treated Net in their household during a defined year, on the basis that one net provides coverage for two people. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202106_Africa_Insecticide_Treated_Net_AccessWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2020temporalResolution:AnnualInterventions__202106_Africa_Insecticide_Treated_Net_AccessRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of Population with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2022This layer map provides estimates of the proportion of population with access to an Insecticide-Treated Net in their household during a defined year, on the basis that one net provides coverage for two people. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202406_Africa_Insecticide_Treated_Net_AccessWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2022temporalResolution:AnnualInterventions__202406_Africa_Insecticide_Treated_Net_AccessRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of Population with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2024This layer map provides estimates of the proportion of population with access to an Insecticide-Treated Net in their household during a defined year, on the basis that one net provides coverage for two people. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202508_Africa_Insecticide_Treated_Net_AccessWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2024temporalResolution:AnnualInterventions__202508_Africa_Insecticide_Treated_Net_AccessRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Proportion of Population with Access to an Insecticide-Treated Net in their Household during a defined year 2000-2025This layer map provides estimates of the proportion of population with access to an Insecticide-Treated Net in their household during a defined year, on the basis that one net provides coverage for two people. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202608_Africa_Insecticide_Treated_Net_AccessWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2025temporalResolution:AnnualInterventions__202608_Africa_Insecticide_Treated_Net_AccessRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Proportion of all population showing, on a given year, detectable Plasmodium vivax parasite 2000-2020This layer map provides estimates of the proportion of all population showing, on a given year, detectable Plasmodium vivax parasite. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time.202206_Global_Pv_Parasite_RateWCSImageMosaictemporalExtent:2000-2020temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalMalaria__202206_Global_Pv_Parasite_RateRectifiedGridCoverage-118.375 -55.0833778381348170.250015258789 53.5416259765625-118.375 -55.0833778381348170.250015258789 53.5416259765625Proportion of all population showing, on a given year, detectable Plasmodium vivax parasite 2000-2022This layer map provides estimates of the proportion of all population showing, on a given year, detectable Plasmodium vivax parasite. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202406_Global_Pv_PRWCSImageMosaictemporalExtent:2000-2022temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalMalaria__202406_Global_Pv_Parasite_RateRectifiedGridCoverage-118.375 -55.0833778381348170.250015258789 53.5416259765625-118.375 -55.0833778381348170.250015258789 53.5416259765625Proportion of all population showing, on a given year, detectable Plasmodium vivax parasite 2000-2024This layer map provides estimates of the proportion of all population showing, on a given year, detectable Plasmodium vivax parasite. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202508_Global_Pv_PRWCSImageMosaictemporalExtent:2000-2024temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalMalaria__202508_Global_Pv_Parasite_RateRectifiedGridCoverage-180.0 -60.0000038146973180.000015258789 85.0-180.0 -60.0000038146973180.000015258789 85.0Proportion of all population showing, on a given year, detectable Plasmodium vivax parasite 2000-2025This layer map provides estimates of the proportion of all population showing, on a given year, detectable Plasmodium vivax parasite. To produce these outputs, we apply geostatistical models to response datasets consisting of PR points and routine surveillance reports, and a rich set of geospatial covariates that characterise habitat for Anopheles mosquitos that spread the disease. The resulting maps are produced annually from year 2000 at a 5x5 km resolution and they illustrate changes in the spatial extent and prevalence of the disease through time. The rasters may be downloaded as a series of multi-band GeoTiffs. The first band consists of the modelled estimates as rates per person [0-1]. The second band is a mask consisting of the values zero and -1; the -1 values represent pixels that we mask out because Pv transmission is very low.202608_Global_Pv_PRWCSImageMosaictemporalExtent:2000-2025temporalResolution:AnnualspatialResolution:5KmspatialExtent:GlobalMalaria__202608_Global_Pv_Parasite_RateRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0000038146973180.000015258789 85.0Proportion of households covered with Indoor Residual Spraying during a defined year 2000-2022The proportion of households covered with Indoor Residual Spraying during a defined year. Indoor Residual Spraying is an operational procedure and strategy for malaria vector control involving spraying interior surfaces of dwellings with a residual insecticide to kill or repel endophilic mosquitoes.202106_Africa_IRS_CoverageWCSImageMosaictemporalResolution:AnnualspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2022Interventions__202106_Africa_IRS_CoverageRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-18.0 -35.052.0416679382324 37.5416679382324Proportion of households covered with Indoor Residual Spraying during a defined year 2000-2024The proportion of households covered with Indoor Residual Spraying during a defined year. Indoor Residual Spraying is an operational procedure and strategy for malaria vector control involving spraying interior surfaces of dwellings with a residual insecticide to kill or repel endophilic mosquitoes.202508_Africa_IRS_CoverageWCSImageMosaictemporalResolution:AnnualspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2024Interventions__202508_Africa_IRS_CoverageRectifiedGridCoverage-18.0000019073486 -35.000003814697352.0416679382324 37.5416679382324-18.0000019073486 -35.000003814697352.0416679382324 37.5416679382324Proportion of households covered with Indoor Residual Spraying during a defined year 2000-2025The proportion of households covered with Indoor Residual Spraying during a defined year. Indoor Residual Spraying is an operational procedure and strategy for malaria vector control involving spraying interior surfaces of dwellings with a residual insecticide to kill or repel endophilic mosquitoes.202608_Africa_IRS_CoverageWCSImageMosaictemporalResolution:AnnualspatialResolution:5KmspatialExtent:AfricatemporalExtent:2000-2025Interventions__202608_Africa_IRS_CoverageRectifiedGridCoverage-18.0000019073486 -35.052.0416679382324 37.5416679382324-18.0000019073486 -35.000003814697352.0416679382324 37.5416679382324Proportion of population that sleeps under an Insecticide-Treated Net during a defined year 2000-2020This layer map provides estimates of the proportion of population that sleeps under an Insecticide-Treated Net. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202106_Africa_Insecticide_Treated_Net_UseWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2020Interventions__202106_Africa_Insecticide_Treated_Net_UseRectifiedGridCoverage-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of population that sleeps under an Insecticide-Treated Net during a defined year 2000-2022This layer map provides estimates of the proportion of population that sleeps under an Insecticide-Treated Net. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202406_Africa_Bednets_UseWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2022Interventions__202406_Africa_Insecticide_Treated_Net_UseRectifiedGridCoverage-18.0 -35.052.0416679382324 37.5416679382324-18.0000648498535 -35.000011444091852.041576385498 37.5416297912598Proportion of population that sleeps under an Insecticide-Treated Net during a defined year 2000-2024This layer map provides estimates of the proportion of population that sleeps under an Insecticide-Treated Net. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202508_Africa_Bednets_UseWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2024Interventions__202508_Africa_Insecticide_Treated_Net_UseRectifiedGridCoverage-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Proportion of population that sleeps under an Insecticide-Treated Net during a defined year 2000-2025This layer map provides estimates of the proportion of population that sleeps under an Insecticide-Treated Net. To produce these estimates, we apply a Bayesian mixed modeling framework built upon data from net manufacturers, national programs, and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in 40 sub-Saharan African countries. The maps are produced annually from year 2000 at a 5x5 km resolution.202608_Africa_Bednets_UseWCSImageMosaicspatialResolution:5KmspatialExtent:AfricatemporalResolution:AnnualtemporalExtent:2000-2025Interventions__202608_Africa_Insecticide_Treated_Net_UseRectifiedGridCoverage-18.0000019073486 -35.052.0416679382324 37.5416679382324-18.0000648498535 -35.000041961669952.0416030883789 37.5416297912598Reproductive Number under Control within the Limits of stable Plasmodium falciparum TransmissionThis layer shows point estimates of basic reproductive number under control within the limits of stable Plasmodium falciparum transmission, illustrating the potential for the disease to spread within a naive population moderated by malaria control202202_Global_Pf_Reproductive_NumberWCSImageMosaicspatialExtent:GlobalspatialResolution:5KmtemporalExtent:2000-2020temporalResolution:AnnualMalaria__202202_Global_Pf_Reproductive_NumberRectifiedGridCoverage-118.375 -55.0833358764648170.25 53.5416679382324-118.375 -55.0833358764648170.25 53.5416679382324Secondary Dominant Vectors<p><small>Secondary dominant malaria vector species in Africa+, 2010</small></p><p><b>This layer shows the set of mosquito species that have been assessed as the most important for malaria transmission in each malaria endemic area.</b></p> <p>We used the predictive maps for each species to generate this multiple vector species map and only those areas with a presence probability greater than 0.5 were included. Our Technical Advisory Group (TAG) identified the three most important vector species per country (where there were three or more species) and ranked these species by their relative importance and the top-ranked species were displayed preferentially. In those areas where the dominance of one species was not clear, the predictive maps for each dominant species were merged.</p> <p><b>Notes on the Asia Pacific</b></p> <p>Due to the complexity of mosquito species found in the Asia Pacific region, all vector species found in each country within the region were ranked by importance. Additionally, where the importance of a vector species is known to vary greatly across a country's geographical extent, more detailed species-specific spatial information was gathered.</p> <p><b>Notes on Africa</b></p> <p>For Africa, the layer illustrates the distribution of those species considered to be of major importance. There are other important vector species so a second map of Africa dedicated to showing the distributions of these 'secondary' dominant vector species is also provided.</p>2010_Secondary-Dominant_Vector_Species_AfricaWCSImageMosaictemplaterastercategory:surfacessubcategory:vector_occurrencesubsubcategory:multiple_vectorsExplorer__2010_Secondary-Dominant_Vector_Species_AfricaRectifiedGridCoverage-19.9583339691162 -35.958335876464855.0 40.0-19.9583333333325 -35.958333333331954.9999999999978 39.9999999999984Sickle Haemoglobin (HbS) Allele FrequencyThis layer map provides estimates of sickle haemoglobin allele frequency. Estimates are based on data from community surveys which fed into a geostatistical model configured to generate gene frequency estimates. The model output a range of estimates (a posterior distribution) at each location (~5x5km) and this layer shows the median value.201201_Global_Sickle_Haemoglobin_HbS_Allele_FrequencyWCSGeoTIFFspatialExtent:GlobalspatialResolution:5KmtemporalExtent:2010Blood_Disorders__201201_Global_Sickle_Haemoglobin_HbS_Allele_FrequencyRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Travel Time to CitiesThis layer map provides estimates of travel time (in minutes) to nearest city to assess inequalities in accessibility in 2015.201501_Global_Travel_Time_to_CitiesWCSGeoTIFFspatialResolution:1KmspatialExtent:GlobaltemporalExtent:2015Accessibility__201501_Global_Travel_Time_to_CitiesRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Travel Time to Nearest Healthcare Facility with Access to Motorized TransportThis layer map provides estimates of time to travel (in minutes) from every point on earth to the nearest healthcare facility by motorized transport. Travel times were calculated to the nearest geolocated hospital or clinic. A critical assumption of the travel time calculations, however, was that people moved through the landscape at an optimal speed. In the optimal friction surface, we assumed that humans traveling along roadways, railways, and on water moved at the motorized movement rates assigned to each type. We utilized healthcare facility locations from two of the largest global databases: (1) OpenStreetMap (OSM) data that was collated and made available for download at www.healthsites.io; and (2) data from Google Maps that was extracted specifically for this project. We augmented the global datasets with continental-scale facility locations that were recently published for Africa (Maina, J. et al., 2019) and Australia (Barbieri, S. & Jorm, 2019). To align temporally, each of these datasets was extracted in mid-2019. To facilitate comparisons between data sources, we used only facilities defined as hospitals and clinics, which matched the definition used in Maina, J. et al. (2019) and Barbieri, S. & Jorm (2019), and was distinguishable within the OSM data by using the facility-type attribute. In contrast, Google Maps data were filtered to isolate hospitals and clinics from the full set of geolocated entities. For each dataset, multiple points found within the same approximately one-by-one-kilometer pixel were merged to match the resolution of the subsequent travel time maps.202001_Global_Motorized_Travel_Time_to_HealthcareWCSGeoTIFFspatialResolution:1KmtemporalExtent:2019spatialExtent:GlobalAccessibility__202001_Global_Motorized_Travel_Time_to_HealthcareRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Walking-only Travel Time to Nearest Healthcare Facility without Access to Motorized TransportThis layer map provides estimates of time to travel (in minutes) from every point on earth to the nearest healthcare facility by walking. Travel times were calculated to the nearest geolocated hospital or clinic. A critical assumption of the travel time calculations, however, was that people moved through the landscape at an optimal speed. In the walking-only friction surface, we assumed that all road travel was limited to a walking speed of five kilometers per hour and all water travel was reduced to the rate for traversing a wetland or swimming of 1.0 kilometer per hour. We utilized healthcare facility locations from two of the largest global databases: (1) OpenStreetMap (OSM) data that was collated and made available for download at www.healthsites.io; and (2) data from Google Maps that was extracted specifically for this project. We augmented the global datasets with continental-scale facility locations that were recently published for Africa (Maina, J. et al., 2019) and Australia (Barbieri, S. & Jorm, 2019). To align temporally, each of these datasets was extracted in mid-2019. To facilitate comparisons between data sources, we used only facilities defined as hospitals and clinics, which matched the definition used in Maina, J. et al. (2019) and Barbieri, S. & Jorm (2019), and was distinguishable within the OSM data by using the facility-type attribute. In contrast, Google Maps data were filtered to isolate hospitals and clinics from the full set of geolocated entities. For each dataset, multiple points found within the same approximately one-by-one-kilometer pixel were merged to match the resolution of the subsequent travel time maps.202001_Global_Walking_Only_Travel_Time_To_HealthcareWCSGeoTIFFspatialExtent:GlobalspatialResolution:1KmtemporalExtent:2019Accessibility__202001_Global_Walking_Only_Travel_Time_To_HealthcareRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0Walking-only travel time to healthcare map without access to motorized transport<p><small>Estimated travel time (minutes) to nearest healthcare facility</small></p><p>This is a map showing the estimated time to travel from every point on earth to the nearest healthcare facility by walking. Travel times were calculated to the nearest geolocated hospital or clinic. A critical assumption of the travel time calculations, however, was that people moved through the landscape at an optimal speed. In the walking-only friction surface, we assumed that all road travel was limited to a walking speed of five kilometers per hour and all water travel was reduced to the rate for traversing a wetland or swimming of 1.0 kilometer per hour.</p> <p>We utilized healthcare facility locations from two of the largest global databases: (1) OpenStreetMap (OSM) data that was collated and made available for download at www.healthsites.io29; and (2) data from Google Maps that was extracted specifically for this project. We augmented the global datasets with continental-scale facility locations that were recently published for Africa (Maina, J. et al., 2019) and Australia (Barbieri, S. & Jorm, 2019). To align temporally, each of these datasets was extracted in mid-2019. To facilitate comparisons between data sources, we used only facilities defined as hospitals and clinics, which matched the definition used in Maina, J. et al. (2019) and Barbieri, S. & Jorm (2019), and was distinguishable within the OSM data by using the facility-type attribute. In contrast, Google Maps data were filtered to isolate hospitals and clinics from the full set of geolocated entities. For each dataset, multiple points found within the same approximately one-by-one-kilometer pixel were merged to match the resolution of the subsequent travel time maps.</p>2020_walking_only_travel_time_to_healthcareWCSGeoTIFFrastercategory:surfacessubcategory:human_populationrPackage:2020_Walking_Only_Travel_Time_To_Healthcaresubsubcategory:accessibility_(2019)Explorer__2020_walking_only_travel_time_to_healthcareRectifiedGridCoverage-180.0 -60.0180.0 85.0-180.0 -60.0180.0 85.0