Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study
In: BMC Medical Informatics and Decision Making ; volume 23, issue 1 ; ISSN 1472-6947, 2023
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Zugriff:
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.
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Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study
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Autor/in / Beteiligte Person: | 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 ; International Development Research Centre ; Styrelsen för Internationellt Utvecklingssamarbete |
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Zeitschrift: | BMC Medical Informatics and Decision Making ; volume 23, issue 1 ; ISSN 1472-6947, 2023 |
Veröffentlichung: | Springer Science and Business Media LLC, 2023 |
Medientyp: | academicJournal |
DOI: | 10.1186/s12911-023-02098-3 |
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