Reconstruct COVID-19 Epidemic Curves in Hubei Province Cities by Adjusting Mobile Population Between Different Cities

• Activity Number: 58 - Advanced Bayesian Topics (Part 1)
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Neda Jalali* and Yang Yang
• Companies: University of Florida and University of Florida
• Keywords: COVID19; SEIR; Transmission Rate; PFMCMC; Compartmental Model; Seroprevalence

Abstract

Background: In the ongoing COVID-19 pandemic, the effects of moving population between different regions and asymptomatic cases have not been fully investigated on the person-to-person transmission rate. In this study, we used a stochastic compartmental model to estimate the transmission rates in different cities of Hubei province by adjusting the role of the moving population and asymptotic cases.

Methods: We used daily migration indexes between different cities of Hubei province collected from mobility data to reconstruct the COVID-19 epidemic curve in each city of Hubei through an age-stratified Susceptible, Asymptomatic and Infectious, Exposed and Infectious (Pre-symptomatic), Infectious (Symptomatic), Recovered (SAEIR) stochastic model. To adjust the asymptomatic cases, we utilized seroprevalence information in our model. Due to different unobserved variables, we implemented a PFMCMC algorithm to infer the data.

Findings: The initial values for the infectious cases in pre-symptomatic, asymptomatic, and symptomatic compartments were zero for all cities except Wuhan. Also, we assumed the transmission rate of the asymptomatic cases is half of the pre-symptomatic and symptomati