Tweet
BMC Med Inform Decis Mak
. 2023 Jan 26;23(1):19.
doi: 10.1186/s12911-023-02098-3.
Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study
Benjamin Lieberman 1 2 , Jude Dzevela Kong 3 4 , Roy Gusinow 5 4 , Ali Asgary 6 4 , Nicola Luigi Bragazzi 3 7 4 , Joshua Choma 5 4 , Salah-Eddine Dahbi 5 4 , Kentaro Hayashi 8 4 , Deepak Kar 5 4 , Mary Kawonga 9 10 4 , Mduduzi Mbada 4 11 , Kgomotso Monnakgotla 5 4 , James Orbinski 4 12 , Xifeng Ruan 5 4 , Finn Stevenson 5 4 , Jianhong Wu 3 7 4 , Bruce Mellado 5 4 13
Affiliations
- PMID: 36703133
- DOI: 10.1186/s12911-023-02098-3
Abstract
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.
Keywords: Artificial intelligence; Big data; COVID-19; Control intervention; Gauteng department of health; Hot-spot; Risk adjusted strategy; South Africa.