Modeling the Impact of COVID-19 Vaccination in Lebanon: A Call to Speed-Up Vaccine Roll Out

  1. Ghina R. Mumtaz
  2. Fadi El-Jardali
  3. Mathilda Jabbour
  4. Aya Harb
  5. Laith J. Abu-Raddad
  6. Monia Makhoul

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Abstract

Four months into the SARS-CoV-2 vaccination campaign, only 10.7% of the Lebanese population have received at least one dose, raising serious concerns over the speed of vaccine roll-out and its impact in the event of a future surge. Using mathematical modeling, we assessed the short-term impact of various vaccine roll-out scenarios on SARS-CoV-2 epidemic course in Lebanon. At current population immunity levels, estimated by the model at 40% on 15 April 2021, a large epidemic wave is predicted if all social distancing restrictions are gradually eased and variants of concern are introduced. Reaching 80% vaccine coverage by the end of 2021 will flatten the epidemic curve and will result in a 37% and 34% decrease in the peak daily numbers of severe/critical disease cases and deaths, respectively; while reaching intermediate coverage of 40% will result in only a 10–11% decrease in each. Reaching 80% vaccine coverage by August would prevent twice as many severe/critical disease cases and deaths than if it were reached by December. Easing restrictions over a longer duration resulted in more favorable vaccination impact. In conclusion, for vaccination to have impact in the short-term, scale-up has to be rapid and reach high coverage (at least 70%), while sustaining social distancing measures during roll-out. At current vaccination pace, this is unlikely to be achieved. Concerted efforts need to be made to overcome local challenges and substantially scale up vaccination to avoid a surge that the country, with its multiple crises and limited health-care capacity, is largely unprepared for.

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  1. Version published to 10.3390/vaccines9070697
  2. ScreenIT

    SciScore for 10.1101/2021.05.27.21257937: (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 analyzed using MATLAB R2019a [26].
    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 some limitations. Model estimations are contingent on the validity and generalizability of input data. While current available natural history and epidemiological evidence was used to justify model assumptions and parameter values, our understanding of this infection is still evolving. Data on SARS-CoV-2 seroprevalence in the Lebanese population are lacking. The level of prior exposure to the infection plays an important role in this analysis and will affect estimates of vaccine impact. We assumed that 20% had been exposed to the infection on January 1,2021, a sensible estimate based on triangulation of available local data, including the cumulative number of confirmed infections and deaths and routine clinical antibody testing data. We assumed that both natural and vaccine-induced immunity last for one year, as suggested by available data [21-24]. However, these two important parameters remain unknown. If they prove to be less than the assumed one year, the impact of the vaccine will be reduced. The model did not prioritize vaccination based on age, since younger individuals in the workforce are currently being vaccinated through the private sector. Given that the highest risk elderly population (>70 years) did receive the vaccine first, our estimates for the impact of vaccination are conservative and the number of averted disease cases and deaths may be higher than the ones reported in our study. Finally, analyses were conducted at a national level, whereas v...

    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

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  3. Version published to 10.1101/2021.05.27.21257937v1 on medRxiv