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Disease Modeling, SVIRD, COVID-19, Public Mandates


Health Policy | Mathematics | Numerical Analysis and Computation | Ordinary Differential Equations and Applied Dynamics | Physical Sciences and Mathematics | Science and Mathematics Education


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.



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