Bionomic database for the dominant vectors of malaria in the Americas

Compilation of bionomics data for the dominant vectors of malaria in the Americas from published sources reporting data collected from 1985-2014

MOESM10 of Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance

Additional file 10. geotiff that can be opened in GIS software such as QGIS ( http://www.qgis.org/ ) or ArcMap ( http://www.esri.com/software/arcgis ). Model output data for An. melas. Band 1 contains the mean values per pixel, band 2 contains the median values, band 3 contains the 2.5% quantile values, and band 4 contains the 97.5% quantile values

MOESM5 of Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance

Additional file 5. Tables of AUC values and top predictors, and maps showing the prediction ranges

MOESM14 of Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance

Additional file 14. geotiff that can be opened in GIS software such as QGIS ( http://www.qgis.org/ ) or ArcMap ( http://www.esri.com/software/arcgis ). Model output data for the Funestus group. Band 1 contains the mean values per pixel, band 2 contains the median values, band 3 contains the 2.5% quantile values, and band 4 contains the 97.5% quantile values

MOESM8 of Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance

Additional file 8. geotiff that can be opened in GIS software such as QGIS ( http://www.qgis.org/ ) or ArcMap ( http://www.esri.com/software/arcgis ). Model output data for An. funestus. Band 1 contains the mean values per pixel, band 2 contains the median values, band 3 contains the 2.5% quantile values, and band 4 contains the 97.5% quantile values

A “species accumulation” curve showing the cumulative number of diseases of interest sampled by increasing numbers of public health stakeholders examined.

<p>The diseases of interest of twenty global health stakeholders was indexed and plotted (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003756#sec005" target="_blank">Methods</a>). As additional organisations are sampled beyond the fifteen used in this study, the number of unique diseases identified plateaus at around 42. Thus not all public health stakeholders need to be sampled to capture the global diversity of diseases of public health interest.</p

Plots indicating the relative importance of each mapping cluster.

<p>(A) Area of each section is determined by the total DALY contribution of each of the 33 clusters. Blue indicates a cluster contributing to the top ten clusters to be prioritised, green indicates top 44 diseases (n = 5 clusters) and light green represents the remaining disease clusters (n = 18). (B) Area of each section is determined by the total DALY contribution of 30 clusters, with HIV, tuberculosis and malaria excluded. Blue indicates a cluster contributing to the top ten clusters to be prioritised (n = 7), green indicates top 44 diseases (n = 5 clusters) and light green represents the remaining disease clusters (n = 18). STH = soil-transmitted helminth, (B)—bacteria, (N)—nematode, (Pl)—platyhelminth, (V)—virus. (C) Area of each section is determined by the total policy interest score of each of the 33 clusters. Red indicates a cluster within the top ten to be prioritised, orange indicates one of top 44 diseases (n = 5) and light pink represents the remaining disease clusters (n = 18). STH = soil-transmitted helminth, (B)—bacteria, (N)—nematode, (Pl)—platyhelminth, (V)—virus.</p

Disease prioritisation.

<p>Plot showing the 33 clusters of diseases as ranked by burden of disease DALYs (y-axis—logarithmic scale) and mean policy priority score of occurrence mapping and prevalence mapping diseases (x-axis—linear scale). The top ten clusters circled and numbered as identified in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003756#pntd.0003756.t001" target="_blank">Table 1</a>. The size of the circle is determined by the total number of diseases contained and colour is based upon taxonomy (as outlined by <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003756#pntd.0003756.g001" target="_blank">Fig 1</a>; the web appendix contains the full disease listing for each cluster). The dashed guidelines are perpendicular to the axis along which prioritisation order for the clusters was determined; those closer to the top right, along this axis, were prioritised higher.</p

Hierarchical organisation of the 33 clusters.

<p>The 176 diseases with strong rationale for mapping were first sorted by taxonomy of pathogenic agent (in orange) and then structured by common epidemiological and transmission characteristics into sub-groupings (in blue) and finally clusters (in red). STH = soil transmitted helminth, VBD = vector borne disease.</p

Clusters indicated as mapping priorities with their constituent diseases recommended for distribution modelling and current global mapping projects identified.

<p>* Indicates default null value.</p><p>MAP—Malaria Atlas Project; WHO—World Health Organization; GBD—Global Burden of Disease; GAHI—Global Atlas of Helminth Infections; SEEG—Spatial Ecology and Epidemiology Group; APOC—African Programme for Onchocerciasis Control; GAT—Global Atlas of Trachoma</p><p>Clusters indicated as mapping priorities with their constituent diseases recommended for distribution modelling and current global mapping projects identified.</p