Speaker
Description
About half of the world's population, about 4 billion people, live in areas with a risk of dengue infection. Recent evolutionary adaptations of dengue-transmitting mosquitoes to colder regions, such as the Himalayas of Nepal, have raised severe public health concerns about dengue pandemics. In this talk, I will demonstrate how machine-learning techniques and mathematical models can be combined to develop valuable tools for describing dengue transmission dynamics. Using dengue and climate data from Nepal and Taiwan, I will present methods for computing dengue-virus transmission reproduction numbers using mathematical models integrated with machine learning. Our methods provide a valuable approach to combining mathematical models and real-time data in a machine-learning framework to identify effective strategies for preventing dengue outbreaks.
