Optimal COVID-19 epidemic control until vaccine deployment

  1. R. Djidjou-Demasse
  2. Y. Michalakis
  3. M. Choisy
  4. M. T. Sofonea
  5. S. Alizon

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Abstract

Since Dec 2019, the COVID-19 epidemic has spread over the globe creating one of the greatest pandemics ever witnessed. This epidemic wave will only begin to roll back once a critical proportion of the population is immunised, either by mounting natural immunity following infection, or by vaccination. The latter option can minimise the cost in terms of human lives but it requires to wait until a safe and efficient vaccine is developed, a period estimated to last at least 18 months. In this work, we use optimal control theory to explore the best strategy to implement while waiting for the vaccine. We seek a solution minimizing deaths and costs due to the implementation of the control strategy itself. We find that such a solution leads to an increasing level of control with a maximum reached near the 16th month of the epidemics and a steady decrease until vaccine deployment. The average containment level is approximately 50% during the 25-months period for vaccine deployment. This strategy strongly out-performs others with constant or cycling allocations of the same amount of resources to control the outbreak. This work opens new perspectives to mitigate the effects of the ongoing COVID-19 pandemics, and be used as a proof-of-concept in using mathematical modelling techniques to enlighten decision making and public health management in the early times of an outbreak.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.02.20049189: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    One of the limitations of our study is that we assumed that in the time necessary for vaccine development, the standard of treatment of COVID-19 infections remains constant. In reality, the time to discover and implement a new treatment could be lower than the time to discover and deploy a vaccine. This would not affect the general picture because the epidemic threat will remain unless the herd immunity threshold is reached. However, it would greatly affect the optimal strategy itself. Nevertheless, this same model remains valid in the context of treatment discovery if we redefine the time interval as the time necessary to set-up such a treatment. Qualitatively, we anticipate our results to hold on a shorter time interval. Indeed, assume that the control is to implement until a deployment of a pharmaceutical intervention within a year (approximately 50 weeks). In such configuration, we find that the optimal control leads to an increasing level of control with a maximum reached near the fourth month of the epidemics and a steady decrease until deployment of a such pharmaceutical intervention (Figure 5). Our results offer new perspectives and research avenues to control the COVID-19 epidemics. In particular, we find that strategies that alleviate epidemic control too early all tend to only delay the epidemic wave. We also see that varying the level of control over time provides the best results for an overall given control effort over the period of interest. An open challenge i...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.04.02.20049189v3 on medRxiv
  3. Version published to 10.1101/2020.04.02.20049189v2 on medRxiv
  4. Version published to 10.1101/2020.04.02.20049189v1 on medRxiv