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Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory

Author

Listed:
  • Michael te Vrugt

    (Westfälische Wilhelms-Universität Münster)

  • Jens Bickmann

    (Westfälische Wilhelms-Universität Münster)

  • Raphael Wittkowski

    (Westfälische Wilhelms-Universität Münster)

Abstract

For preventing the spread of epidemics such as the coronavirus disease COVID-19, social distancing and the isolation of infected persons are crucial. However, existing reaction-diffusion equations for epidemic spreading are incapable of describing these effects. In this work, we present an extended model for disease spread based on combining a susceptible-infected-recovered model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. We show that the model exhibits interesting transient phase separation associated with a reduction of the number of infections, and allows for new insights into the control of pandemics.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19024-0
    DOI: 10.1038/s41467-020-19024-0
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    Cited by:

    1. 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.
    2. Xiyun Zhang & Zhongyuan Ruan & Muhua Zheng & Jie Zhou & Stefano Boccaletti & Baruch Barzel, 2022. "Epidemic spreading under mutually independent intra- and inter-host pathogen evolution," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Igor Gadelha Pereira & Joris Michel Guerin & Andouglas Gonçalves Silva Júnior & Gabriel Santos Garcia & Prisco Piscitelli & Alessandro Miani & Cosimo Distante & Luiz Marcos Garcia Gonçalves, 2020. "Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
    4. Anindya Ghose & Heeseung Andrew Lee & Wonseok Oh & Yoonseock Son, 2024. "Leveraging the Digital Tracing Alert in Virus Fight: The Impact of COVID-19 Cell Broadcast on Population Movement," Information Systems Research, INFORMS, vol. 35(2), pages 570-589, June.
    5. Chen, Kexin & Pun, Chi Seng & Wong, Hoi Ying, 2023. "Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 84-98.
    6. 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).
    7. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    8. Panicker, Akhil & Sasidevan, V., 2024. "Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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