Super-Spreaders Out, Super-Spreading In: The Effects of Infectiousness Heterogeneity and Lockdowns on Herd Immunity

  1. Jhonatan Tavori
  2. Hanoch Levy

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Abstract

Recently, [8] has proposed that heterogeneity of infectiousness (and susceptibility) across individuals in infectious diseases, plays a major role in affecting the Herd Immunity Thresh-old (HIT). Such heterogeneity has been observed in COVID-19 and is recognized as overdis-persion (or “super-spreading”). The model of [8] suggests that super-spreaders contribute significantly to the effective reproduction factor, R , and that they are likely to get infected and immune early in the process. Consequently, under R 0 ≈ 3 (attributed to COVID-19), the Herd Immunity Threshold (HIT) is as low as 5%, in contrast to 67% according to the traditional models [1, 2, 4, 10].

This work follows up on [8] and proposes that heterogeneity of infectiousness (susceptibility) has two “faces” whose mix affects dramatically the HIT: (1) Personal-Trait- , and (2) Event-Based- Infectiousness (Susceptibility). The former is a personal trait of specific individuals ( super-spreaders ) and is nullified once those individuals are immune (as in [8]). The latter is event-based (e.g cultural super-spreading events) and remains effective throughout the process, even after the super-spreaders immune. We extend [8]’s model to account for these two factors, analyze it and conclude that the HIT is very sensitive to the mix between (1) and (2), and under R 0 ≈ 3 it can vary between 5% and 67%. Preliminary data from COVID-19 suggests that herd immunity is not reached at 5%.

We address operational aspects and analyze the effects of lockdown strategies on the spread of a disease. We find that herd immunity (and HIT) is very sensitive to the lock-down type. While some lockdowns affect positively the disease blocking and increase herd immunity, others have adverse effects and reduce the herd immunity.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.23.21250242: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: 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: 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

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  2. Version published to 10.1101/2021.01.23.21250242v1 on medRxiv