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

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dc.contributor.authorWoods, Ethan
dc.contributor.authorHan, David
dc.date.accessioned2023-02-17T16:57:30Z
dc.date.available2023-02-17T16:57:30Z
dc.date.issued2022-12
dc.description.abstractThe 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.en_US
dc.description.departmentManagement Science and Statisticsen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1751
dc.language.isoen_USen_US
dc.publisherUTSA Office of Undergraduate Researchen_US
dc.relation.ispartofseriesThe UTSA Journal of Undergraduate Research and Scholarly Work;Volume 8
dc.subjectundergraduate student worksen_US
dc.subjectCOVID-19en_US
dc.subjectepidemiology of COVID-19en_US
dc.subjectmaximum likelihood estimationen_US
dc.subjectmitigation strategiesen_US
dc.subjectsusceptible-infected-recovered modelen_US
dc.titlePolicy-Guided Susceptible-Infected-Recovered Modeling of the COVID-19 Spread in Texasen_US
dc.typePosteren_US

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