Big Data- and Artificial Intelligence-Based Hot-Spot Analysis of COVID-19: Gauteng, South Africa, as a case study

Abstract

“Coronavirus Disease 2019” (COVID-19) related data contain many complexities that must be taken into account when extracting information to guide public health decision- and policy-makers. In generalising the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. This statistically random spread of a virus through a population is the core of the majority of Susceptible-Infectious-Recovered-Deceased (SIRD) models and is dependent on factors such as number of infected cases, infection rate, level of social interactions, susceptible population and total population. However, the spread of COVID-19 and, therefore, the data representing the virus progression do not always conform to a stochastic model. In this paper, we have focused on the most influential non-stochastic dynamics of COVID-19, hot-spots, utilizing artificial intelligence (AI) based geo-localization and clustering analyses, taking Gauteng (South Africa) as a case study.