Policy-Guided Susceptible-Infected-Recovered Modeling of the COVID-19 Spread in Texas

Date
2022-12
Authors
Woods, Ethan
Han, David
Journal Title
Journal ISSN
Volume Title
Publisher
UTSA Office of Undergraduate Research
Abstract

The goal of this research was to create an SIR model for the Texas COVID-19 cases based on the state data from March of 2020 through October of 2020, and to investigate the impact of public policies on the transmission of COVID. The data was pre-processed using Excel; some basic time series graphs were produced in Excel as well. All other data analysis, including the production of all graphs relating to the SIR model, was performed in R. Difficulty in estimating the model parameters by the maximum likelihood method was encountered due to the short durations between the implementation dates of various policies designed to curb the spread of COVID-19. Examining the estimate trends of beta, gamma, and R0, a stabilizing pattern for R0 was observed over time, which would require further investigations to understand the epidemiology of COVID-19 in Texas.

Description
Keywords
undergraduate student works, COVID-19, epidemiology of COVID-19, maximum likelihood estimation, mitigation strategies, susceptible-infected-recovered model
Citation
Department
Management Science and Statistics
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