Beyond the new normal: Assessing the feasibility of vaccine-based suppression of SARS-CoV-2

  1. Madison Stoddard
  2. Sharanya Sarkar
  3. Lin Yuan
  4. Ryan P. Nolan
  5. Douglas E. White
  6. Laura F. White
  7. Natasha S. Hochberg
  8. Arijit Chakravarty

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

As the COVID-19 pandemic drags into its second year, there is hope on the horizon, in the form of SARS-CoV-2 vaccines which promise disease suppression and a return to pre-pandemic normalcy. In this study we critically examine the basis for that hope, using an epidemiological modeling framework to establish the link between vaccine characteristics and effectiveness in bringing an end to this unprecedented public health crisis. Our findings suggest that a return to pre-pandemic social and economic conditions without fully suppressing SARS-CoV-2 will lead to extensive viral spread, resulting in a high disease burden even in the presence of vaccines that reduce risk of infection and mortality. Our modeling points to the feasibility of complete SARS-CoV-2 suppression with high population-level compliance and vaccines that are highly effective at reducing SARS-CoV-2 infection. Notably, vaccine-mediated reduction of transmission is critical for viral suppression, and in order for partially-effective vaccines to play a positive role in SARS-CoV-2 suppression, complementary biomedical interventions and public health measures must be deployed simultaneously.

Article activity feed

  1. Version published to 10.1371/journal.pone.0254734
  2. ScreenIT

    SciScore for 10.1101/2021.01.27.20240309: (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

    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:
    Our goal in this work was not to predict the future broadly, but rather to focus narrowly on potential limitations of the first wave of vaccines, and ask “what if” questions around the epidemiological consequences of these potential limitations. A significant potential limitation of the first wave of SARS-CoV-2 vaccines is the failure to sufficiently prevent infection and transmission in the vaccinated population. While this has not been demonstrated conclusively, it remains a plausible risk that has been observed for other vaccines in the past and that is not being addressed directly in the vast majority of ongoing clinical vaccine studies [6,56]. In this study, we have used mathematical modeling to understand conditions in the United States at steady state, if a vaccine that was unable to prevent infection and exerted limited reduction in transmission was used as the primary or sole public-health intervention. Our results paint a grim picture: at steady state, we could expect 30 million symptomatic COVID-19 cases a year for a vaccine that is 95% effective at reducing symptoms and that is taken by 80% of the population (using best-estimate assumptions of natural immunity that lasts for 18 months, permanent vaccine immunity and an R0 of 5.7). If the reduction in risk of symptoms was concomitant with a reduction in risk of mortality (a best-case assumption), this scenario would lead to 240,000 deaths per year, making COVID-19 the third leading cause of death in the United Stat...

    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

    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 10.1101/2021.01.27.20240309 on medRxiv