Modelling the effects of global climate change on Chikungunya transmission in the 21st century

Abstract

The arrival and rapid spread of the mosquito-borne viral disease Chikungunya across the Americas is one of the most significant public health developments of recent years, preceding and mirroring the subsequent spread of Zika. Globalization in trade and travel can lead to the importation of these viruses, but climatic conditions strongly affect the efficiency of transmission in local settings. In order to direct preparedness for future outbreaks, it is necessary to anticipate global regions that could become suitable for Chikungunya transmission. Here, we present global correlative niche models for autochthonous Chikungunya transmission. These models were used as the basis for projections under the representative concentration pathway (RCP) 4.5 and 8.5 climate change scenarios. In a further step, hazard maps, which account for population densities, were produced. The baseline models successfully delineate current areas of active Chikungunya transmission. Projections under the RCP 4.5 and 8.5 scenarios suggest the likelihood of expansion of transmission-suitable areas in many parts of the world, including China, sub-Saharan Africa, South America, the United States and continental Europe. The models presented here can be used to inform public health preparedness planning in a highly interconnected world.

Figure 1

Chikungunya under the baseline and RCP 8.5 climate change scenarios in Asia and Australasia. Left: Climatic suitability, right: hazard index. Climate change scenarios represent the mean model output obtained through the 5 GCMs. Climatic suitability output is scaled to the over-all global minimum (0) and maximum (0.623) values observed in any model. Maps were generated using the “raster” package in R 3.3.2 (https://www.r-project.org/) and QGIS 2.8.1 (https://www.qgis.org/).

Figure 2

Chikungunya under the baseline and RCP 8.5 climate change scenarios in Africa. Left: Climatic suitability, right: hazard index. Climate change scenarios represent the mean model output obtained through the 5 GCMs. Climatic suitability output is scaled to the over-all global minimum (0) and maximum (0.623) values observed in any model. Maps were generated using the “raster” package in R 3.3.2 (https://www.r-project.org/) and QGIS 2.8.1 (https://www.qgis.org/).

Figure 3

Chikungunya under the baseline and RCP 8.5 climate change scenarios in North- and Central America. Left: Climatic suitability, right: hazard index. Climate change scenarios represent the mean model output obtained through the 5 GCMs. Climatic suitability output is scaled to the over-all global minimum (0) and maximum (0.623) values observed in any model. Maps were generated using the “raster” package in R 3.3.2 (https://www.r-project.org/) and QGIS 2.8.1 (https://www.qgis.org/).

Figure 4

Chikungunya under the baseline and RCP 8.5 climate change scenarios in South America. Left: Climatic suitability, right: hazard index. Climate change scenarios represent the mean model output obtained through the 5 GCMs. Climatic suitability output is scaled to the over-all global minimum (0) and maximum (0.623) values observed in any model. Maps were generated using the “raster” package in R 3.3.2 (https://www.r-project.org/) and QGIS 2.8.1 (https://www.qgis.org/).

Figure 5

Chikungunya under the baseline and RCP 8.5 climate change scenarios in Europe. Left: Climatic suitability, right: hazard index. Climate change scenarios represent the mean model output obtained through the 5 GCMs. Climatic suitability output is scaled to the over-all global minimum (0) and maximum (0.623) values observed in any model. Maps were generated using the “raster” package in R 3.3.2 (https://www.r-project.org/) and QGIS 2.8.1 (https://www.qgis.org/).