Work

Fitting a COVID-19 Model Incorporating Senses of Safety and Caution to Local Data from Spartanburg County, South Carolina

Year: 2024

Type: article

Abstract: Common mechanistic models include Susceptible-Infected-Removed (SIR) and Susceptible-Exposed-Infected-Removed (SEIR) models. These models in their basic forms have generally failed to capture the nature of the COVID-19 pandemic's multiple waves and do not take into account public policies such as social distancing, mask mandates, and the ``Stay-at-Home'' orders implemented in early 2020. While the Susceptible-Vaccinated-Infected-Recovered-Deceased (SVIRD) model only adds two more compartments to the SIR model, the inclusion of time-dependent parameters allows for the model to better capture the first two waves of the COVID-19 pandemic when surveillance testing was common practice for a large portion of the population. We find that the SVIRD model with time-dependent and piecewise parameters accurately fits the 2019-2020 experimental data from Spartanburg County, South Carolina. These additions give insight into the changing social response toward the COVID-19 pandemic within Spartanburg County.

Source: CODEE Journal

DOI: 10.5642/codee.hrxa6019

Authors: D. Griffin, Amanda Mangum

Institutions: Brown University, Converse College

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: National Institutes of Health, National Institute of General Medical Sciences