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Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation

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  • Moritz Kersting

    (Next Generation Mobility Group, Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization
    Chair of Regional Management and Business Promotion, Faculty of Resource Management, HAWK University for Applied Sciences and Art
    Technical University of Dresden
    Technical University of Dresden)

  • Andreas Bossert

    (Next Generation Mobility Group, Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization
    Technical University of Dresden
    Technical University of Dresden
    Center of Methods in Social Sciences, Department of Social Sciences, Georg-August-University of Göttingen)

  • Leif Sörensen

    (Next Generation Mobility Group, Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization
    Technical University of Dresden
    Technical University of Dresden
    Thinktank of Aeronautics, Aerodynamics and Aerospace Technology)

  • Benjamin Wacker

    (Next Generation Mobility Group, Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization
    Department of Engineering and Natural Sciences, University of Applied Sciences Merseburg)

  • Jan Chr. Schlüter

    (Next Generation Mobility Group, Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization
    Technical University of Dresden
    Technical University of Dresden
    Institute for the Dynamics of Complex Systems, Faculty of Physics, Georg-August-University of Göttingen)

Abstract

COVID-19 has spread rapidly around the globe. While there has been a slow down of the spread in some countries, e.g., in China, the African continent is still at the beginning of a potentially wide spread of the virus. Owing to its economic strength and imbalances, South Africa is of particular relevance with regard to the drastic measures to prevent the spread of this novel coronavirus. In March 2020, South Africa imposed one of the most severe lockdowns worldwide and subsequently faced the number of infections slowing down considerably. In May 2020, this lockdown was partially relaxed and further easing of restrictions was envisaged. In July and August 2020, daily new infections peaked and declined subsequently. Lockdown measures were further relaxed. This study aims to assess the recent and upcoming measures from an epidemiological perspective. Agent-based epidemic simulations are used to depict the effects of policy measures on the further course of this epidemic. The results indicate that measures that are either lifted too early or are too lenient have no sufficient mitigating effects on infection rates. Consequently, continuous exponential infection growth rates or a second significant peak of infected people occur. These outcomes are likely to cause higher mortality rates once healthcare capacities are occupied and no longer capable to treat all severely and critically infected COVID-19 patients. In contrast, strict measures appear to be a suitable way to contain the virus. The simulations imply that the initial lockdown of 27 March 2020 was probably sufficient to slow the growth in the number of infections, but relaxing countermeasures might allow for a second severe outbreak of COVID-19 in our investigated simulation region of Nelson Mandela Bay Municipality.

Suggested Citation

  • Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00830-w
    DOI: 10.1057/s41599-021-00830-w
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    1. John E. Ataguba, 2020. "COVID-19 Pandemic, a War to be Won: Understanding its Economic Implications for Africa," Applied Health Economics and Health Policy, Springer, vol. 18(3), pages 325-328, June.
    2. Flaminio Squazzoni & J. Gareth Polhill & Bruce Edmonds & Petra Ahrweiler & Patrycja Antosz & Geeske Scholz & Emile Chappin & Melania Borit & Harko Verhagen & Francesca Giardini & Nigel Gilbert, 2020. "Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(2), pages 1-10.
    3. Jürgen Hackl & Thibaut Dubernet, 2019. "Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models," Future Internet, MDPI, vol. 11(4), pages 1-14, April.
    4. Jun Wu & Boyun Liang & Cunrong Chen & Hua Wang & Yaohui Fang & Shu Shen & Xiaoli Yang & Baoju Wang & Liangkai Chen & Qi Chen & Yang Wu & Jia Liu & Xuecheng Yang & Wei Li & Bin Zhu & Wenqing Zhou & Hua, 2021. "SARS-CoV-2 infection induces sustained humoral immune responses in convalescent patients following symptomatic COVID-19," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Michael te Vrugt & Jens Bickmann & Raphael Wittkowski, 2020. "Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    6. Silva, Petrônio C.L. & Batista, Paulo V.C. & Lima, Hélder S. & Alves, Marcos A. & Guimarães, Frederico G. & Silva, Rodrigo C.P., 2020. "COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Ian Bannon & Paul Collier, 2003. "Natural Resources and Violent Conflict : Options and Actions," World Bank Publications - Books, The World Bank Group, number 15047.
    8. Yoshiyuki Sugishita & Junko Kurita & Tamie Sugawara & Yasushi Ohkusa, 2020. "Effects of voluntary event cancellation and school closure as countermeasures against COVID-19 outbreak in Japan," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-10, December.
    9. Xingjie Hao & Shanshan Cheng & Degang Wu & Tangchun Wu & Xihong Lin & Chaolong Wang, 2020. "Reconstruction of the full transmission dynamics of COVID-19 in Wuhan," Nature, Nature, vol. 584(7821), pages 420-424, August.
    10. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    11. Michael Greenstone & Vishan Nigam, 2020. "Does Social Distancing Matter?," Working Papers 2020-26, Becker Friedman Institute for Research In Economics.
    12. Davide Scarselli & Nazmi Burak Budanur & Marc Timme & Björn Hof, 2021. "Discontinuous epidemic transition due to limited testing," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    13. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
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