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Optimal timing of one-shot interventions for epidemic control

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  • Francesco Di Lauro
  • István Z Kiss
  • Joel C Miller

Abstract

The interventions and outcomes in the ongoing COVID-19 pandemic are highly varied. The disease and the interventions both impose costs and harm on society. Some interventions with particularly high costs may only be implemented briefly. The design of optimal policy requires consideration of many intervention scenarios. In this paper we investigate the optimal timing of interventions that are not sustainable for a long period. Specifically, we look at at the impact of a single short-term non-repeated intervention (a “one-shot intervention”) on an epidemic and consider the impact of the intervention’s timing. To minimize the total number infected, the intervention should start close to the peak so that there is minimal rebound once the intervention is stopped. To minimise the peak prevalence, it should start earlier, leading to initial reduction and then having a rebound to the same prevalence as the pre-intervention peak rather than one very large peak. To delay infections as much as possible (as might be appropriate if we expect improved interventions or treatments to be developed), earlier interventions have clear benefit. In populations with distinct subgroups, synchronized interventions are less effective than targeting the interventions in each subcommunity separately.Author summary: Some interventions which help control a spreading epidemic have significant adverse effects on the population, and cannot be maintained long-term. The optimal timing of such an intervention will depend on the ultimate goal.Interventions to delay the epidemic while new treatments or interventions are developed are best implemented as soon as possible.Interventions to minimize the peak prevalence are best implemented partway through the growth phase allowing immunity to build up so that the eventual rebound is not larger than the initial peak.Interventions to minimize the total number of infections are best implemented late in the growth phase to minimize the amount of rebound.

Suggested Citation

  • Francesco Di Lauro & István Z Kiss & Joel C Miller, 2021. "Optimal timing of one-shot interventions for epidemic control," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-24, March.
    • Handle: RePEc:plo:pcbi00:1008763
    DOI: 10.1371/journal.pcbi.1008763
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    1. T Déirdre Hollingsworth & Don Klinkenberg & Hans Heesterbeek & Roy M Anderson, 2011. "Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    2. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2018. "Control fast or control smart: When should invading pathogens be controlled?," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-21, February.
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    1. Li, Qian & Xiao, Yanni, 2023. "Analysis of a hybrid SIR model combining the fixed-moments pulse interventions with susceptibles-triggered threshold policy," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    2. Florin Avram & Rim Adenane & Lasko Basnarkov & Gianluca Bianchin & Dan Goreac & Andrei Halanay, 2023. "An Age of Infection Kernel, an R Formula, and Further Results for Arino–Brauer A , B Matrix Epidemic Models with Varying Populations, Waning Immunity, and Disease and Vaccination Fatalities," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    3. Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    4. Li, Tangjuan & Xiao, Yanni, 2023. "Optimal strategies for coordinating infection control and socio-economic activities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 533-555.

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