Endemicity Is Not a Victory: The Unmitigated Downside Risks of Widespread SARS-CoV-2 Transmission

  1. Madison Stoddard
  2. Alexander Novokhodko
  3. Sharanya Sarkar
  4. Debra Van Egeren
  5. Laura F. White
  6. Natasha S. Hochberg
  7. Michael S. Rogers
  8. Bruce Zetter
  9. Diane Joseph-McCarthy
  10. Arijit Chakravarty

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Abstract

The strategy of relying solely on current SARS-CoV-2 vaccines to halt SARS-CoV-2 transmission has proven infeasible. In response, many public-health authorities have advocated for using vaccines to limit mortality while permitting unchecked SARS-CoV-2 spread (“learning to live with the disease”). The feasibility of this strategy critically depends on the infection fatality rate (IFR) of SARS-CoV-2. An expectation exists that the IFR will decrease due to selection against virulence. In this work, we perform a viral fitness estimation to examine the basis for this expectation. Our findings suggest large increases in virulence for SARS-CoV-2 would result in minimal loss of transmissibility, implying that the IFR may vary freely under neutral evolutionary drift. We use an SEIRS model framework to examine the effect of hypothetical changes in the IFR on steady-state death tolls under COVID-19 endemicity. Our modeling suggests that endemic SARS-CoV-2 implies vast transmission resulting in yearly US COVID-19 death tolls numbering in the hundreds of thousands under many plausible scenarios, with even modest increases in the IFR leading to unsustainable mortality burdens. Our findings highlight the importance of enacting a concerted strategy and continued development of biomedical interventions to suppress SARS-CoV-2 transmission and slow its evolution.

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  1. Version published to 10.3390/covid2120121
  2. ScreenIT

    SciScore for 10.1101/2022.03.29.22273146: (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
    We implemented both distributions in Python to assess loss of transmissibility due to fatal COVID-19 outcomes.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The likelihood of fatal outcome over time is represented by a log-logistic distribution in the Scipy stats module with scale 31.18, shape 6.80, and loc parameter –14.51 according to Bai et al 43.
    Scipy
    suggested: (SciPy, RRID:SCR_008058)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our work has several key limitations. The model linking reinfection frequency, IFR and death toll is not age stratified, so it does not account for greater vaccine uptake in older populations. As a result, the model may overstate the death tolls, as risk of death is strongly age-dependent in COVID-19 89. Offsetting this limitation is the finding that the benefit of vaccinal immunity appears to be age-dependent 90, and so the higher vaccine uptake in older populations may be undermined by a lower level of vaccinal efficacy over time. Additionally, the model does not account for evolution-mediated vaccine resistance or waning of vaccinal immunity and thus assumes vaccines retain their high efficacy over the simulation interval. This is likely to be an optimistic assumption and will also have the impact of mitigating death tolls. Our work does not explicitly model vaccines or boosters-a full exploration of the impact of vaccines on viral evolution is outside the scope of this work but explored by us elsewhere (manuscript in preparation). Similarly, the interplay between the kinetics of antibody decay and population heterogeneity in the rate of waning of natural and vaccinal immunity will impact the level of protection that vaccines provide, and a full treatment of these effects is outside the scope of this work but described in a different work by us (manuscript in preparation). As is true of all SEIR-type models, ours assumes homogenous population mixing and thereby overestimat...

    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.

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  3. Version published to 10.1101/2022.03.29.22273146v1 on medRxiv