Future Risk from the Ae. aegypti Vector: Modeling the Effects of Climate Change and Human Population Density on Habitat Suitability

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Introduction: The Aedes aegypti mosquito is responsible for the transmission of Yellow Fever, Dengue, Chikungunya and Zikavirus, making it a deadly vector and global public health threat. Zikavirus and Chikungunya, which were previously restricted to smaller geographic areas, have both appeared in the Western Hemisphere in the past three years and spread to areas where A. aegypti are present. This means that the pathogens have now entered areas in which the population has no previous immunity, which can lead to extensive outbreaks and epidemics. As the effects of global climate change become apparent, the areas of the globe that are suitable for inhabitance by A. aegypti may change. Additionally, this vector prefers human hosts for blood meals and requires standing water to breed, which is often created by water storage containers. This means that increasing urbanization and human population density are likely to put populations at higher risk of exposure to this vector. Methods: To create maps of the future risk of exposure to Aedes aegypti globally, species occurrence data for the vector and the Maxent modeling approach were used. Current and projected climate data were downloaded from WorldClim.org for the four representative concentration pathways (RCPs) used to model future climate change. Human population density, projected to 2050, the same timeframe as the future climate data, were used to model changes in human populations. To identify areas at high risk for future presence of A. aegypti populations, current and future models were compared across areas with at least a 50% probability of increased risk. These results where then used to create maps displaying high risk areas. Results: The AUC, an indicator of model fit, signaled that the models had high predictive power. However, high omission rates indicated that the trade-off of risk mapping may be a need to decrease probability thresholds below 50% to capture the full at-risk population. Future high-risk areas were most often those surrounding current cities, which supports the idea that the combination of urbanization and increasing human population density will work synergistically to increase the disease burden within and around urban centers. Additionally, expansion at the current geographic margins of this species shows that incursion into currently non-endemic areas is possible. Conclusions: Urban and peri-urban populations are likely to be at higher risk of exposure compared to rural areas due to global climate change and changes in population density. Attempts to model expansion of vector habitats should consider how these human population characteristics will change the risk to populations and how to best identify the areas at highest risk. Thresholds for the probability of a population being at risk of exposure to a vector may need to be different from those required to determine whether or not a habitat is suitable for a species. Appropriately determining which areas are high-risk results in maps and models can then be used to identify areas where climate change mitigation and vector control efforts are likely to have the highest impacts.


Johnson City, TN

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