Adherence and sustainability of interventions informing optimal control against the COVID-19 pandemic

  1. Laura Di Domenico
  2. Chiara E. Sabbatini
  3. Pierre-Yves Boëlle
  4. Chiara Poletto
  5. Pascal Crépey
  6. Juliette Paireau
  7. Simon Cauchemez
  8. François Beck
  9. Harold Noel
  10. Daniel Lévy-Bruhl
  11. Vittoria Colizza

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Abstract

Background

After one year of stop-and-go COVID-19 mitigation, in the spring of 2021 European countries still experienced sustained viral circulation due to the Alpha variant. As the prospect of entering a new pandemic phase through vaccination was drawing closer, a key challenge remained on how to balance the efficacy of long-lasting interventions and their impact on the quality of life.

Methods

Focusing on the third wave in France during spring 2021, we simulate intervention scenarios of varying intensity and duration, with potential waning of adherence over time, based on past mobility data and modeling estimates. We identify optimal strategies by balancing efficacy of interventions with a data-driven “distress” index, integrating intensity and duration of social distancing.

Results

We show that moderate interventions would require a much longer time to achieve the same result as high intensity lockdowns, with the additional risk of deteriorating control as adherence wanes. Shorter strict lockdowns are largely more effective than longer moderate lockdowns, for similar intermediate distress and infringement on individual freedom.

Conclusions

Our study shows that favoring milder interventions over more stringent short approaches on the basis of perceived acceptability could be detrimental in the long term, especially with waning adherence.

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  1. Version published to 10.1038/s43856-021-00057-5
  2. ScreenIT

    SciScore for 10.1101/2021.05.13.21257088: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Contacts at school, work and on transports are considered according to the French school calendar, school closures, and presence at workplaces estimated by Google.
    Google
    suggested: (Google, RRID:SCR_017097)

    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:
    Our study has a set of limitations. It is applied to a region only, as indicators for France hide a variable situation at the local level, limiting the accuracy of modeling approaches extended to the whole country. Geographical heterogeneity depends on the current epidemic situation, population immunity due to natural infection, and variant frequency, so that results are not directly generalizable to other regions. We did not consider waning of immunity48 or reinfections over the time frames modeled. We assumed the transmissibility advantage of the B.1.1.7 variant from early estimates in France9, in agreement with other studies1,2, however this may be altered over time by social distancing and competition with other strains. Assuming a smaller transmissibility advantage for the variant would lead to lower incidence projections, however it would not be able to capture the evolution in time of B.1.1.7 frequency in the region. We did not consider the interaction with other variants, such as 20H/501Y.V2 or 20J/501Y.V3, that are already present in the country and show so far limited diffusion. If these variants can at least partially escape natural or vaccine-induced immunity49, they may pose a challenge for the management of the epidemic as population immunity increases. Our approach is not suited to account for contacts in low-risk and high-risk conditions, e.g. in closed ill aerated settings vs. open settings, but seasonal reductions effectively account for these aspects. Model...

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.05.13.21257088v1 on medRxiv