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Mechanistic modelling of COVID-19 and the impact of lockdowns on a short-time scale

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  • Danish A Ahmed
  • Ali R Ansari
  • Mudassar Imran
  • Kamal Dingle
  • Michael B Bonsall

Abstract

Background: To mitigate the spread of the COVID-19 coronavirus, some countries have adopted more stringent non-pharmaceutical interventions in contrast to those widely used. In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies also have been implemented, such as the total lockdown of fragmented regions, which are composed of sparsely and highly populated areas. Methods: In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host is infected if in close spatial proximity of the infectious host with an assigned transmission probability. Our focus is on a short-time scale (∼ 3 days), which is the average time lag time before an infected individual becomes infectious. Results: We find that the level of infection remains approximately constant with an increase in population diffusion, and also in the case of faster population dispersal (super-diffusion). Moreover, we demonstrate how the efficacy of imposing a lockdown depends heavily on how susceptible and infectious individuals are distributed over space. Conclusion: Our results indicate that on a short-time scale, the type of movement behaviour does not play an important role in rising infection levels. Also, lock-down restrictions are ineffective if the population distribution is homogeneous. However, in the case of a heterogeneous population, lockdowns are effective if a large proportion of infectious carriers are distributed in sparsely populated sub-regions.

Suggested Citation

  • Danish A Ahmed & Ali R Ansari & Mudassar Imran & Kamal Dingle & Michael B Bonsall, 2021. "Mechanistic modelling of COVID-19 and the impact of lockdowns on a short-time scale," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-20, October.
  • Handle: RePEc:plo:pone00:0258084
    DOI: 10.1371/journal.pone.0258084
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    References listed on IDEAS

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    Cited by:

    1. 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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