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The social dynamics of COVID-19

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  • Lux, Thomas

Abstract

We add a simple dynamic process for adaptive “social distancing” measures to a standard SIR model of the COVID pandemic. With a limited attention span and in the absence of a consistent long-term strategy against the pandemic, this process leads to a sweeping of an instability, i.e. fluctuations in the effective reproduction number around its bifurcation value of Reff=1. While mitigating the pandemic in the short-run, this process remains intrinsically fragile and does not constitute a sustainable strategy that societies could follow for an extended period of time.

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  • Lux, Thomas, 2021. "The social dynamics of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120310086
    DOI: 10.1016/j.physa.2020.125710
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    References listed on IDEAS

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    1. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    2. Stauffer, Dietrich & Sornette, Didier, 1999. "Self-organized percolation model for stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 271(3), pages 496-506.
    3. Florian Dorn & Sahamoddin Khailaie & Marc Stöckli & Sebastian Binder & Berit Lange & Patrizio Vanella & Timo Wollmershäuser & Andreas Peichl & Clemens Fuest & Michael Meyer-Hermann, 2020. "Das gemeinsame Interesse von Gesundheit und Wirtschaft: Eine Szenarienrechnung zur Eindämmung der Corona- Pandemie," ifo Schnelldienst Digital, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 1(06), May.
    4. Egenter, E. & Lux, T. & Stauffer, D., 1999. "Finite-size effects in Monte Carlo simulations of two stock market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 268(1), pages 250-256.
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    Citations

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    Cited by:

    1. Nascimento, Diego C. & Pimentel, Bruno A. & Souza, Renata M.C.R. & Costa, Lilia & Gonçalves, Sandro & Louzada, Francisco, 2021. "Dynamic graph in a symbolic data framework: An account of the causal relation using COVID-19 reports and some reflections on the financial world," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    2. Patrick Mellacher, 2022. "Endogenous viral mutations, evolutionary selection, and containment policy design," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(3), pages 801-825, July.
    3. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    4. Chen, Zhenxi & Zheng, Huanhuan, 2022. "Herding in the Chinese and US stock markets: Evidence from a micro-founded approach," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 597-604.
    5. Burridge, James & Gnacik, Michał, 2022. "Public efforts to reduce disease transmission implied from a spatial game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    6. Pascoal, R. & Rocha, H., 2022. "Population density impact on COVID-19 mortality rate: A multifractal analysis using French data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

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