Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement

  1. Zachary Susswein
  2. Shweta Bansal

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Superspreading is a ubiquitous feature of SARS-CoV-2 transmission dynamics, with a few primary infectors leading to a large proportion of secondary infections. Despite the superspreading events observed in previous coronavirus outbreaks, the mechanisms behind the phenomenon are still poorly understood. Here, we show that superspreading is largely driven by heterogeneity in contact behavior rather than heterogeneity in susceptibility or infectivity caused by biological factors. We find that highly heterogeneous contact behavior is required to produce the extreme superspreading estimated from recent COVID-19 outbreaks. However, we show that superspreading estimates are noisy and subject to biases in data collection and public health capacity, potentially leading to an overestimation of superspreading. These results suggest that superspreading for COVID-19 is substantial, but less than previously estimated. Our findings highlight the complexity inherent to quantitative measurement of epidemic dynamics and the necessity of robust theory to guide public health intervention.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.08.20246082: (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 generate random graphs of size N = 5000 in Python using the package “networkx”.
    Python
    suggested: (IPython, RRID:SCR_001658)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    These limitations point to the need for public availability of large-scale contact tracing datasets and a more thorough examination of biases in k estimation. Our results suggest that public health interventions meant to decrease the rate of superspreading events (e.g. social distancing, bar closures) may not materially alter measurements of superspreading (i.e. k) even though they are effective at decreasing infection rates. These interventions are undoubtedly important techniques in the public health arsenal, reducing rates of spread and total disease burden, and should be diligently applied and followed. However, a superspreading pathogen (k < 1) is dependent on substantial contact heterogeneity and even large reductions in contact heterogeneity are unlikely to decrease k by more than 0.1-0.3 when the initial k is less than 1; rapid decrease of k only occurs at smaller values of contact heterogeneity which do not produce much superspreading. For this reason, interventions such as social distancing may not meaningfully decrease measurements of k in superspreading diseases, even though they provide substantial benefit through reduction in R0. Interventions to decrease transmissibility (e.g. mask wearing) may also have limited impact on superspreading quantification due to the insensitivity of k to transmissibility. However, even though these interventions may not decrease k, they may decrease superspreading – because R0 and k are dependent on each other, descriptors of the o...

    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.

  2. Version published to 10.1101/2020.12.08.20246082v1 on medRxiv