BNT162b2 mRNA vaccinations in Israel: understanding the impact and improving the vaccination policies by redefining the immunized population

  1. Chana Ross
  2. Oren Spector
  3. Meytal Avgil Tsadok
  4. Yossi Weiss
  5. Royi Barnea

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Abstract

By the end of February 2021, when 48% of the Israeli population was immune, the number of new positive COVID-19 cases significantly dropped across all ages. Understanding which parameters influenced this drop and how to minimize the number of hospitalizations and overall positive cases is urgently needed.

In this study we conducted an observational analysis which included COVID-19 data with over 12,000,000 PCR tests from 250 cities in Israel. In addition, we performed a simulation of different vaccination campaigns to find the optimal policy.

Our analysis revealed that cities with younger populations reached a decrease in new cases when a lower percentage of their residents were immunized, showing that median age is a crucial parameter effecting overall immunity, while other parameters appeared to be insignificant. This variance between cities is explained by recalculating the immunized population and multiplying each individual by a factor symbolizing the impact of their age on the spread on the virus. This factor is easily calculated from historical data of positive cases per age.

The simulation proves that prioritizing different age groups or changing the rate of vaccinations drastically effects the overall hospitalizations and positive cases.

One-Sentence Summary

understanding what influences reaching covid-19 overall immunity and how to maximize the effect of the vaccination campaign.

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    SciScore for 10.1101/2021.06.08.21258471: (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 used the FARZ python package to model social interactions between different age groups.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 13 and 15. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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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  2. Version published to 10.1101/2021.06.08.21258471v1 on medRxiv