Fitting a COVID-19 Model Incorporating Senses of Safety and Caution to Local Data from Spartanburg County, South Carolina
Jan 4, 2024·,·
1 min read
Griffin, D. Chloe
Mangum, Amanda J.
Model fit to Spartanburg County COVID-19 data (see paper).Abstract
We fit a deterministic SVIRD model (an extension of SIR/SEIR models) with time-dependent parameters to local COVID-19 case data from Spartanburg County, South Carolina. The model incorporates dynamic population behaviors—namely a sense of safety and a level of caution—that evolve with case counts and vaccination rollout, enabling improved fits to multi-wave pandemic data and insights into the effects of testing availability and behavioral change on inferred infection prevalence.
Type
Publication
CODEE Journal
This article fits a deterministic SVIRD model (Susceptible–Vaccinated–Infected–Recovered–Deceased) with time-dependent, piecewise parameters to county-level COVID-19 case data from Spartanburg County, South Carolina. The study compares alternative mechanistic models (SIR, SEIR, SVIRD), examines parameter-fitting approaches, and discusses the implications of limited testing and changing public behavior on inferred epidemic dynamics. The full text and PDF are available from the CODEE Journal repository. We also presented these results at the JMM (program included).