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Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19

Author

Listed:
  • Lukas Zenk

    (Department of Knowledge and Communication Management, Faculty of Business and Globalization, Danube University Krems, 3500 Krems an der Donau, Austria
    These authors contributed equally to this work.)

  • Gerald Steiner

    (Department of Knowledge and Communication Management, Faculty of Business and Globalization, Danube University Krems, 3500 Krems an der Donau, Austria
    Complexity Science Hub Vienna, 1090 Vienna, Austria
    These authors contributed equally to this work.)

  • Miguel Pina e Cunha

    (Nova School of Business and Economics, Universidade Nova de Lisboa, 2775-405 Carcavelos, Portugal)

  • Manfred D. Laubichler

    (Complexity Science Hub Vienna, 1090 Vienna, Austria
    School of Complex Adaptive Systems Tempe, Arizona State University, Tempe, AZ 85287-2701, USA
    Santa Fe Institute, Santa Fe, NM 87501, USA
    Global Climate Forum, 10178 Berlin, Germany)

  • Martin Bertau

    (Institute of Chemical Technology, Freiberg University of Mining and Technology, 09599 Freiberg, Germany)

  • Martin J. Kainz

    (WasserCluster Lunz-Inter-University Center for Aquatic Ecosystem Research, 3293 Lunz am See, Austria)

  • Carlo Jäger

    (Complexity Science Hub Vienna, 1090 Vienna, Austria
    School of Complex Adaptive Systems Tempe, Arizona State University, Tempe, AZ 85287-2701, USA
    Global Climate Forum, 10178 Berlin, Germany
    Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China)

  • Eva S. Schernhammer

    (Complexity Science Hub Vienna, 1090 Vienna, Austria
    Department of Epidemiology, Center for Public Health, Medical University of Vienna, 1090 Vienna, Austria
    Channing Division of Network Medicine, Harvard Medical School, Boston, MA 02115, USA
    These authors contributed equally to this work.)

Abstract

Although the first coronavirus disease 2019 (COVID-19) wave has peaked with the second wave underway, the world is still struggling to manage potential systemic risks and unpredictability of the pandemic. A particular challenge is the “superspreading” of the virus, which starts abruptly, is difficult to predict, and can quickly escalate into medical and socio-economic emergencies that contribute to long-lasting crises challenging our current ways of life. In these uncertain times, organizations and societies worldwide are faced with the need to develop appropriate strategies and intervention portfolios that require fast understanding of the complex interdependencies in our world and rapid, flexible action to contain the spread of the virus as quickly as possible, thus preventing further disastrous consequences of the pandemic. We integrate perspectives from systems sciences, epidemiology, biology, social networks, and organizational research in the context of the superspreading phenomenon to understand the complex system of COVID-19 pandemic and develop suggestions for interventions aimed at rapid responses.

Suggested Citation

  • Lukas Zenk & Gerald Steiner & Miguel Pina e Cunha & Manfred D. Laubichler & Martin Bertau & Martin J. Kainz & Carlo Jäger & Eva S. Schernhammer, 2020. "Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19," IJERPH, MDPI, vol. 17(21), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7884-:d:435695
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    2. Xiaozhi Huang & Xiaojie Zhang & Heng Zhang, 2022. "The Impact of Mixed Emotions on Consumer Improvisation Behavior in the Environment of COVID-19: The Moderating Effect of Tightness-Looseness Culture," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    3. Giovanni Gabutti & Erica d’Anchera & Francesco De Motoli & Marta Savio & Armando Stefanati, 2021. "The Epidemiological Characteristics of the COVID-19 Pandemic in Europe: Focus on Italy," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    4. Wang, Richard & Ye, Zhongnan & Lu, Miaojia & Hsu, Shu-Chien, 2022. "Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling," Applied Energy, Elsevier, vol. 322(C).

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