A vaccination model for COVID-19 in Gauteng, South Africa

Christina J Edholm; Benjamin Levy; Lee Spence; Folashade B Agusto; Faraimunashe Chirove; C Williams Chukwu; David Goldsman; Moatlhodi Kgosimore; Innocent Maposa; K A Jane White; Suzanne Lenhart

doi:10.1016/j.idm.2022.06.002

A vaccination model for COVID-19 in Gauteng, South Africa

Infect Dis Model. 2022 Sep;7(3):333-345. doi: 10.1016/j.idm.2022.06.002. Epub 2022 Jun 9.

Authors

Affiliations

¹ Department of Mathematics, Scripps College, Claremont, CA, USA.
² Mathematics Department, Fitchburg State University, Fitchburg, MA, USA.
³ Department of Mathematics, University of Tennessee, Knoxville, TN, USA.
⁴ Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA.
⁵ Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa.
⁶ H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
⁷ Biometry and Mathematics Department, Botswana University of Agriculture and Natural Resources, Gaborone, Botswana.
⁸ Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁹ Department of Mathematical Sciences, University of Bath, Bath, UK.

Abstract

The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.

Keywords: 34A34; 92D30; COVID-19; Gauteng; ODE epidemiology Model; Parameter estimation; South Africa; Vaccination.