Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study
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 occu...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02098-3 |
| _version_ | 1811175825460953088 |
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| author | Benjamin Lieberman Jude Dzevela Kong Roy Gusinow Ali Asgary Nicola Luigi Bragazzi Joshua Choma Salah-Eddine Dahbi Kentaro Hayashi Deepak Kar Mary Kawonga Mduduzi Mbada Kgomotso Monnakgotla James Orbinski Xifeng Ruan Finn Stevenson Jianhong Wu Bruce Mellado |
| author_facet | Benjamin Lieberman Jude Dzevela Kong Roy Gusinow Ali Asgary Nicola Luigi Bragazzi Joshua Choma Salah-Eddine Dahbi Kentaro Hayashi Deepak Kar Mary Kawonga Mduduzi Mbada Kgomotso Monnakgotla James Orbinski Xifeng Ruan Finn Stevenson Jianhong Wu Bruce Mellado |
| author_sort | Benjamin Lieberman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T19:43:17Z |
| format | Article |
| id | doaj.art-9539e1a324c341828e4500cde2dbdd7d |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-10T19:43:17Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-9539e1a324c341828e4500cde2dbdd7d2023-01-29T12:14:10ZengBMCBMC Medical Informatics and Decision Making1472-69472023-01-0123111510.1186/s12911-023-02098-3Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case studyBenjamin Lieberman0Jude Dzevela Kong1Roy Gusinow2Ali Asgary3Nicola Luigi Bragazzi4Joshua Choma5Salah-Eddine Dahbi6Kentaro Hayashi7Deepak Kar8Mary Kawonga9Mduduzi Mbada10Kgomotso Monnakgotla11James Orbinski12Xifeng Ruan13Finn Stevenson14Jianhong Wu15Bruce Mellado16School of Physics and Institute for Collider Particle Physics, University of the WitwatersrandDepartment of Mathematics and Statistics, York UniversitySchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandDisaster and Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-response Simulation, York UniversityDepartment of Mathematics and Statistics, York UniversitySchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandSchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandSchool of Computer Science and Applied Mathematics, University of the WitwatersrandSchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandSchool of Public Health, University of the WitwatersrandAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC)School of Physics and Institute for Collider Particle Physics, University of the WitwatersrandAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC)School of Physics and Institute for Collider Particle Physics, University of the WitwatersrandSchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandDepartment of Mathematics and Statistics, York UniversitySchool of Physics and Institute for Collider Particle Physics, University of the WitwatersrandAbstract 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.https://doi.org/10.1186/s12911-023-02098-3COVID-19South AfricaGauteng department of healthHot-spotRisk adjusted strategyControl intervention |
| spellingShingle | Benjamin Lieberman Jude Dzevela Kong Roy Gusinow Ali Asgary Nicola Luigi Bragazzi Joshua Choma Salah-Eddine Dahbi Kentaro Hayashi Deepak Kar Mary Kawonga Mduduzi Mbada Kgomotso Monnakgotla James Orbinski Xifeng Ruan Finn Stevenson Jianhong Wu Bruce Mellado Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study BMC Medical Informatics and Decision Making COVID-19 South Africa Gauteng department of health Hot-spot Risk adjusted strategy Control intervention |
| title | Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study |
| title_full | Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study |
| title_fullStr | Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study |
| title_full_unstemmed | Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study |
| title_short | Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study |
| title_sort | big data and artificial intelligence based hot spot analysis of covid 19 gauteng south africa as a case study |
| topic | COVID-19 South Africa Gauteng department of health Hot-spot Risk adjusted strategy Control intervention |
| url | https://doi.org/10.1186/s12911-023-02098-3 |
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