Large Environmental and Demographic Data Sets in Models for Mosquito Borne Disease Risk in Brazil and Ecuador

The spread of mosquito borne diseases is complex and direct measurements of the fundamental mechanisms of spread is an onerous task. Therefore, we must turn to proxy data to gain insights into their relationships. Fortunately, fewer limitations in data collection and storage have resulted in an abundance of rich, diverse, and dynamic data sources that measure various aspects of mosquito borne disease spread. Weather station measurements and remote sensing of vegetation health can elucidate the status of the mosquito habitat, while demographic indicators can provide information about the impact of the manmade infrastructure on mosquito spread. Despite the abundance of proxy data, there are many challenges due to the heterogeneity in these data streams. For example, it is difficult to determine which variables, or collection of variables, are useful given the large number of observations and potential redundancy. In order to address these challenges, we developed an iterative dimension reduction method using hierarchical clustering to decrease the number of variables, while maintaining the intrinsic information of the full data set. Our method is applied to dengue transmission in Brazil and Ecuador and is tailored to maintain biological interpretation of variable sets driving mosquito borne diseases such as demographic and environmental data. We find that socioeconomic factors, temperature, and levels of healthy vegetation are highly predictive of dengue incidence. Our results are consistent with previous studies that have shown that each of these factors are impacted by climate change in ways that will further increase mosquito-borne diseases incidence around the world. Our study results can inform short-term prevention strategies as well as long term public health campaigns focused on reducing the overall burden of mosquito borne diseases.