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David Han

The University of Texas at San Antonio



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Ethan Woods

The University of Texas at San Antonio



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Maximum Likelihood Estimation for the Policy-Guided Susceptible-Infected-Recovered Model of the COVID-19 Cases in Texas

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Keywords: COVID-19 pandemic, epidemiology, maximum likelihood estimation, susceptible-infected-recovered model

David Han

The University of Texas at San Antonio

Ethan Woods

The University of Texas at San Antonio

The goal of this research is to create a Susceptible-Infected-Recovered (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-19. 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 the open-source statistical programming language 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.

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