Sources and methods:
The data used to visualize national vector-borne disease risks due to climate change were derived from a compilation of predictive maps from suitable studies found through a literature search involving geospatial modeling of habitat suitability of vectors and/or pathogens. These studies utilized a range of internationally accepted climate models by CSIRO, Hadley and CCCma, for example, which further utilized a range of climate change scenarios/Representative Concentration Pathways established by the Intergovernmental Panel on Climate Change. The predictive time scale of these studies ranged from the years 2020 to 2080. Geographic scales and units of study ranged from counties to meters. Once identified, the published maps were georeferenced and areas with spreading and/or intensifying disease risks were isolated and used to build a compilation of general future disease risks to the country, visualized here. Considering the range of attributes used to build these predictive models and maps, the visuals presented here do not represent a single future scenario but paint a broad picture of possible future threats.
View supplemental information with the full list of mapping data and disease information sources here.
Tools used to generate map data were ESRI ArcMap 10.5.1 and Adobe Photoshop CC 2017.
Photo credits:
Ixodes scapularis by Patrick Randall CC BY-NC-SA.
Aedes aegypti from E. A. Goeldi (1905) Os Mosquitos no Pará. Memorias do Museu Goeldi. Pará, Brazil., Figure 2 from Plate 1 in the Appendix CC0.
Lutzomyia longipalpis from Ray Wilson, Liverpool School of Tropical Medicine – (2009) PLoS Pathogens Issue Image – Vol. 5(8) August 2009. PLoS Pathog 5(8): ev05.i08. CC BY.
Anopheles quadrimaculatus by Edward McCellan, USCDCP CC0.
Lone star tick by CDC CC0.
Oropsylla Montana flea by Kat Masback CC BY-SA.
Triatoma gerstaeckeri by Drriss & Marrionn CC BY-NC-SA.
Culex pipiens by Alvesgaspar CC BY-SA.