In this project, we aim to build a new physics based and data driven spatiotemporal nonlocal models in order to study the spread of Covid-19 and its societal impact, particularly on the healthcare system. Nonlocal effects are important in our increasingly connected world and are particularly evident in the SAR2-Cov-2 virus outbreak leading to the current Covid-19 pandemic. We will carry out model and algorithm development by utilizing our previous research experience in nonlocal modeling and simulations. We will work with officials of the NYC's Department of Health and Mental Hygiene and other collaborators to analyze and assess the spatial and temporal data from the frontline and in the city's database to calibrate and validate our models so we can make more reliable and more robust projections that can serve as references to plan for ongoing treatment and intervention measures as well as policies on opening-up that are affecting the lives of millions in coming weeks and months. The project will also examine data collection and reporting protocols and the effectiveness of various public policies and medical guidelines so we may mitigate the impact of the virus outbreak.
