Talk Title and Abstract
Title: Way to Fight Globally Rising Dengue: Mathematical Modeling Perspectives
Abstract: About half of the world’s population, approximately 4 billion people, live in areas with a risk of dengue infection. The recent evolutionary adaptation of dengue-transmitting mosquitoes to colder places, such as the Himalayas of Nepal, has raised severe public health concerns about dengue pandemics, demanding urgent responses, such as the WHO’s launch of the Global Strategic Preparedness, Readiness, and Response Plan (SPRP) to tackle dengue.
In this talk, I will demonstrate how mathematical modeling and machine-learning approaches can be valuable tools for designing optimal strategies to prevent and control dengue. In particular, I will present data-driven mathematical models of climate-influenced dengue transmission, computations of dengue reproduction numbers, math-model-informed neural networks (MINN), and healthcare accessibility in Nepalese communities.
Our results provide critical insights into the role of climate change in shifting dengue-transmitting mosquitoes and related epidemics in colder places and help establish an evidence-based framework for formulating policy-design models to allocate healthcare facilities for preventing and managing dengue outbreaks.