Parameters and descriptions of the ABM model.

Since the discovery of COVID-19 in Wuhan, China in 2019, close to seven million people have died from the infection. At the onset of the pandemic, many countries enacted stringent measures such as school and event closings in a bid to control and curtail the spread of the virus, leading to many within-household infections as people spent more time at home. This study develops an agent-based model (ABM) to gain insight into the impact of government COVID-19 mitigation guidelines and policy options on within-household and community COVID-19 infections in Gauteng, South Africa. Gauteng is the province in South Africa having the smallest land area, but it accounts for 25.8% of the country’s population. Agents are randomly assigned to cells on a square grid varying according to Gauteng’s population density and household size distribution. We found that the percentage of within-household infections is higher in communities with smaller population densities, with the reverse being true for communities with larger population densities. Furthermore, as the agents’ movement activation rate increases, community-related infections increase, especially in communities with small population densities. Our study found an interesting phenomenon, observed for the first time: the existence of a movement activation threshold where the percentage and number of outside household infections overtake the percentage and number of within household infections when the activation rate increases. Lastly, our simulation results captured the two epidemic peaks experienced in Gauteng from March 30, 2020 to June 22, 2021 while varying quarantine violation and movement activation rates. Thus, the developed ABM can be used to exploit the implications of COVID-19 mitigation guidelines and policy options on household transmission to provide interesting insights.