Student Research Reports

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With the hastening progression of climate change, mosquitoes are becoming increasingly lethal to humans: the range, breeding season, and susceptibility for mosquito outbreaks have been on the rise around the world. Taking this into consideration, we sought to develop a model that could forecast mosquito outbreaks in a designated region, given weather and climate data. Because factors such as temperature, precipitation, humidity, and wind are known to affect mosquito oviposition rate, we decided to ascertain their usefulness as predictive indicators of mosquito outbreaks. The main objective of the project was to develop a predictive model to forecast mosquito outbreaks in Eastern Texas. We first sought to obtain the data from NASA POWER, the Google Earth Engine, and fieldwork reports obtained by us and other SEES interns. Next, we iterated through the data sets, searching for factors that could potentially contribute to mosquito outbreaks. Although this process is currently hard-coded, we hope to utilize a more efficient algorithm for doing so in the future. Finally, we plan to refine the model as needed, verifying our model’s results with the fieldwork data. Ideally, we plan on improving the model to utilize more precise information regarding weather, climate, and other dynamic variables, such as the lag time it takes an adult mosquito to develop from an egg. Due to time limitations, we were unable to finalize the working prototype - we have only developed a conceptual model. Once the model is finalized, we hope to continuously refine it to incorporate a larger geographic scope with more data sets from different sources, including citizen science. Ultimately, we hope our observations from the data we collected will allow us to recognize trends that are associated with mosquito outbreaks and their breeding habits, and help us work to aid local communities to identify crises before they occur.