Threshold Dynamics of Disease Transmission via Math-model Informed Neural Networks (MINN)
Infectious diseases have caused the most devastating impacts on humankind. Threshold criteria often describe the disease transmission dynamics and can be used to evaluate public health policies for preventing epidemics or pandemics. In this talk, I will present mathematical models to formulate threshold indices determining the global dynamics of air-borne, environment-transmitted, and vector-borne disease spread. I will demonstrate how a novel math-model informed neural network (MINN)-based method helps estimate threshold indices and predict outbreaks. Our method provides a valuable approach to combining mathematical models and real-time data in a neural network framework to identify effective strategies for preventing disease outbreaks.