Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses

  1. Monia Makhoul
  2. Houssein H. Ayoub
  3. Hiam Chemaitelly
  4. Shaheen Seedat
  5. Ghina R. Mumtaz
  6. Sarah Al-Omari
  7. Laith J. Abu-Raddad

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Abstract

This study aims to inform SARS-CoV-2 vaccine development/licensure/decision-making/implementation, using mathematical modeling, by determining key preferred vaccine product characteristics and associated population-level impacts of a vaccine eliciting long-term protection. A prophylactic vaccine with efficacy against acquisition (VES) ≥70% can eliminate the infection. A vaccine with VES <70% may still control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, if it is supplemented with <20% reduction in contact rate, or if it is complemented with herd-immunity. At VES of 50%, the number of vaccinated persons needed to avert one infection is 2.4, and the number is 25.5 to avert one severe disease case, 33.2 to avert one critical disease case, and 65.1 to avert one death. The probability of a major outbreak is zero at VES ≥70% regardless of the number of virus introductions. However, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. In addition to the reduction in infection acquisition, developers should assess the natural history and disease progression outcomes when evaluating vaccine impact.

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  1. Version published to 10.3390/vaccines8040668
  2. ScreenIT

    SciScore for 10.1101/2020.04.19.20070805: (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
    The model was coded, fitted, and analysed using MATLAB R2019a [35].
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    This study has limitations. Model estimations are contingent on validity and generalizability of input data. While we used available evidence for SARS-CoV-2 natural history and epidemiology, our understanding of the epidemiology is still evolving. We assessed vaccine impact using China as an illustrative example, given the advanced epidemic cycle, yet evidence suggests that many infections may have been undocumented in this country, particularly in the early epidemic phase [46]. This may affect some estimates, such as mortality, probably towards overestimation [34, 47]. While the absolute impact of the vaccine on disease severity and mortality may have been overestimated, the relative impact (reduction rate) is less likely to have been affected. Our baseline R0 for China was 2.1 [34], but R0 may vary across settings, thus affecting estimates for the minimum efficacy needed for infection elimination. For instance, for an R0 of 3, the minimum VES needed for elimination is about 90% (Figure S12 of SM). We assessed vaccine impact for one epidemic cycle, with no assessment of seasonality or future cycles. We assumed a long duration of vaccine protection (10 years), but this has limited impact on the predictions for one epidemic cycle, provided the duration of vaccine protection is greater than one year. Despite these limitations, our model was complex enough to factor the different key vaccine product characteristics, but also parsimonious enough to be tailored to the nature of av...

    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.

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  3. Version published to 10.1101/2020.04.19.20070805v1 on medRxiv