Aedes Mosquito Early Warning System: Challenges in Pre-Processing Climate Data

The Aedes mosquito is a known vector for several diseases, including dengue, chikungunya, yellow fever, and Zika. This vector's geographic distribution and population size depend on several environmental and meteorological factors, including precipitation, temperature, and humidity. An Early Warning System (EWS) for mosquito population prediction is a vital public health tool that can improve planning efforts to reduce the risk of diseases associated with the Aedesmosquito. While several EWSs exist, they focus on specific geographic regions due to limitations posed by the availability and processing effort required to make global, regional, and local datasets usable to the prediction models used by the system. The development of a worldwide EWS can be modified to work with different geographic and regional contexts but lacks the methods for efficiently integrating environmental datasets with incompatible spatial and temporal resolutions. My research aims to create data processing pipelines to extract and process region-specific data for use by an existing EWS prediction engine. To this end, I am developing computational tools to search, pull, and process environmental data from an extensive repository of globally available data into consistent, machine-readable formats using the Google Earth Engine platform. This research is necessary for developing a global EWS that can adapt to specific regional or local contexts.