Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study

• Published in: BMC medical informatics and decision making 2023-01, Vol.23 (1), p.19-19, Article 19
• Main Authors: Lieberman, Benjamin, Kong, Jude Dzevela, Gusinow, Roy, Asgary, Ali, Bragazzi, Nicola Luigi, Choma, Joshua, Dahbi, Salah-Eddine, Hayashi, Kentaro, Kar, Deepak, Kawonga, Mary, Mbada, Mduduzi, Monnakgotla, Kgomotso, Orbinski, James, Ruan, Xifeng, Stevenson, Finn, Wu, Jianhong, Mellado, Bruce
• Format: Article
• Language: English
• Subjects: Analysis; Artificial Intelligence; Big Data; Case reports; Clustering; Clusters; Control intervention; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disease hot spots; Disease transmission; Epidemics; Epidemiology; Evaluation; Gauteng department of health; Health informatics; Hot-spot; Humans; Localization; Pandemics; Probabilistic models; Public health; Risk adjusted strategy; Severe acute respiratory syndrome coronavirus 2; South Africa; South Africa - epidemiology; Statistical analysis; Stochastic models; Stochastic processes; Viral diseases; Viruses
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02098-3

The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing 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. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot.