Mosquito Forecast and Prevention Using 3d printed Ovi-traps and Time Series Analysis with Machine Learning

<br>Dengue fever, transmitted by the Aedes aegypti mosquito, rapidly spreads in Taiwan, particularly in Tainan and Kaohsiung. This study focuses on Tainan First Senior High School, aiming to analyze mosquito activity and develop effective prevention strategies by integrating advanced machine learning techniques. Using a hybrid model that combines SARIMA and Random Forests, we forecast mosquito populations based on environmental data and mosquito counts. This data-driven approach enables precise, real-time predictions of mosquito densities, guiding the strategic placement of 3Dprinted ovitraps to monitor and control populations for school administrators. Our methodology demonstrates the utility of machine learning in public health by possible targeted interventions to mitigate vector-borne diseases in educational settings. Paving the way for future possibilities of integrating machine learning to push the initiative for Sustainable Development Goals.