A0877
Title: ODE-on-scalar regression with an application on COVID-19 data
Authors: Peng Zeng - Auburn University (United States) [presenting]
Abstract: Since the outbreak in late December 2019, COVID-19 quickly spread around the world. Different countries observed slightly different patterns of how the disease spread within their borders. We focus on the confirmed cases in the first 30 days after the first occurrence was reported in a country. The primary goal is to understand how the spread of COVID-19 is affected by socioeconomic indicators of a country. The spread of COVID-19 is modeled by the Susceptible-Infected-Recovered (SIR) model. The problem is formulated as a regression with the response being a function determined by an ODE system and the predictors being scalars. A Bayesian approach is proposed to fit this ODE-on-scalar regression. A Metropolis-Hasting algorithm is designed to sample from the posterior distributions.
