The Aedes aegypti mosquito is the vector for four infectious diseases of global concern – Yellow Fever, Dengue, Chikungunya, and Zikavirus. Previous attempts to model the expansion of the vector habitat due to global climate change have rarely included characteristics related to the human populations on which this mosquito is dependent. The purpose of this research was to determine whether the inclusion of human population density improves model performance while creating risk maps that can be used to determine where humans are most likely to be exposed to the vector in the future. The resulting model demonstrated that the inclusion of human population density improves the predictive power for A. aegypti and should be considered during model development. Maps produced by the model were also suitable for identifying regions where human populations are most likely to experience increased risk. Finally, two areas at risk of expansion were highlighted as a case study in pairing risk mapping with evidence-based intervention strategies to identify sites that would benefit from mosquito-control efforts. In this case, a low-cost program of insecticide-treated covers for water storage containers would likely work well in both Minas Gerais, Brazil and Northwestern Province, Zambia to mitigate mosquito risk. This research demonstrates that human population characteristic not only improve model fit but also increase the extent to which risk maps are actionable by aiding in targeting interventions.
The mosquito Aedes aegypti has long been a vector for human illness in the Southeastern United States. In the past, it has been responsible for outbreaks of dengue, chikungunya, and yellow fever and, very recently, the Zika virus that has been introduced to the region. Multiple studies have modeled the geographic distribution of Ae. aegypti as a function of climate factors; however, this ignores the importance of humans to the anthropophilic biter. Furthermore, Ae. aegypti thrives in areas where humans have created standing water sites, such as water storage containers and trash. As models are developed to examine the potential impact of climate change, it becomes increasingly important to include the most comprehensive set of predictors possible.This study uses Maxent, a species distribution model, to evaluate the effects of adding poverty and population density to climate-only models. Performance was evaluated through model fit statistics, such as AUC, omission, and commission, as well as individual variable contributions and response curves. Models which included both population density and poverty exhibited better predictive power and produced more precise distribution maps. Furthermore, the two human population characteristics accounted for much of the model contribution-more so than climate variables.Modeling mosquito distributions without accounting for their dependence on local human populations may miss factors that are very important to niche realization and subsequent risk of infection for humans. Further research is needed to determine if additional human characteristics should be evaluated for model inclusion.
