The COVID-19 pandemic in South Africa provided an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions. The change in individuals behavior in response to the early-stage COVID-19 outbreak and the change in government policies saw the rate of disease spread in South Africa varying over time. A mathematical model on the outbreak in the province of Gauteng, in South Africa with parameters vary over time is used to explore using data to give the approximate dates of parameter changes and those dates were linked to government policies and consequently assess the impact of each policy. We also test the impact of implementing two different vaccines at varying times to quantify the impact of different government policies and demonstrate how vaccinations can alter infection.

Dr. Faraimunashe Chirove is a NRF C2 rated applied mathematician in South Africa who is holder of a Bachelor of Science honors in Mathematics (2003) and Master of Science in Mathematics (2005) from University of Zimbabwe and a PhD (2011) in Mathematics (Mathematical Biology) from University of Botswana. He is currently employed as a Senior lecturer at University of Johannesburg. His vision is to be one of the leading bio-mathematicians both nationally and internationally producing high quality and high impact research. He is currently doing most of his research on mathematical modelling of infectious and zoonotic infections at both cellular and population levels. His research interests are expanding into mathematical ecology, data-based modelling, agent-based modelling, , stochastic modelling, multi-scale modelling, applications into zoonotic diseases, antimicrobial resistance in agricultural settings and, systems mathematical biology. He has published 25 peer reviewed articles and one book chapter in his career and also trained and graduated eight PhD students and 13 MSc students. He is also focusing on multi-and interdisciplinary research as to make his modelling skills manifest into realistic impact on public health and animal health policies.

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