Including Human Population Characteristics in Ecological Niche Models for Aedes aegypti when Modeling Projected Disease Risk due to Climate ChangeAssociation of American Geographers 2017 Annual Meeting
DescriptionThe Aedes aegypti mosquito is responsible for transmission of four vector-borne diseases that cause considerable global morbidity and mortality. Projections of the future effects of global climate change indicate that expansion of this species due to changing habitats is possible. Furthermore, since A. aegypti is highly dependent on human populations for feeding and egg-laying sites, changing human population characteristics are likely to alter the risk of exposure for humans based on geographic location. This study aims to create future potential risk maps for human exposure to A. aegypti using human population density as a predictor. Using current population density data and future growth trajectories, high-resolution human population density forecasts were created for 2050, then included as variables in ecological niche models developed using Maxent. Species occurrence data and high resolution climate data for current and future conditions (best and worst case scenarios) were included in the model, as well. Model fit indices and variable contributions indicated that the inclusion of human population density improves model accuracy for A. aegypti. Risk maps created by these models showed that areas currently adjacent to large cities within endemic regions, such as central Africa and western Brazil, are likely to see the greatest increase in risk to human populations. This corroborates current projections on increasing urbanization in the future and suggests that these models can be used to target interventions in high risk areas.
Citation InformationJulie Obenauer, Megan Quinn, Ying Li and Andrew Joyner. "Including Human Population Characteristics in Ecological Niche Models for Aedes aegypti when Modeling Projected Disease Risk due to Climate Change" Association of American Geographers 2017 Annual Meeting (2017)
Available at: http://works.bepress.com/ying-li/51/