Currently, about half of the world’s population, about 4 billion people, live in areas with a risk of dengue infection, with further increased public health concerns due to recent evolutionary adaptation of dengue-transmitting mosquitoes to colder places. This project develops and uses mathematical models and computational methods, including the novel Math-model Informed Neural Networks (MINN) based on emerging mathematics in mosquito-dengue biology. Proper management guidelines, identified through data-driven models, help healthcare professionals mitigate the burdens of dengue infection, thereby improving the quality of life for dengue-infected patients and their families. The outcomes of this project not only fundamentally advance the fields of mathematical biology and quantitative biology but also have a simultaneous broad and highly positive societal impact. In addition, this project offers extensive interdisciplinary research training opportunities for undergraduate and graduate students in mathematics and biology. The project will expand research and educational opportunities to various programs for students, as well as junior and senior researchers, and will incorporate the research into an interdisciplinary mathematical biology course. This project will focus on three aims: (a) Develop Math-model Informed Neural Networks (MINN) capturing emerging mathematics in climate-dependent mosquito-dengue biology. (b) Analyze models and develop MINN-based methods to estimate