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Using secondary cases to characterize the severity of an emerging or re-emerging infection

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  • Tim K. Tsang

    (The University of Hong Kong,
    Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories)

  • Can Wang

    (The University of Hong Kong,)

  • Bingyi Yang

    (The University of Hong Kong,)

  • Simon Cauchemez

    (Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS)

  • Benjamin J. Cowling

    (The University of Hong Kong,
    Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories)

Abstract

The methods to ascertain cases of an emerging infectious disease are typically biased toward cases with more severe disease, which can bias the average infection-severity profile. Here, we conducted a systematic review to extract information on disease severity among index cases and secondary cases identified by contact tracing of index cases for COVID-19. We identified 38 studies to extract information on measures of clinical severity. The proportion of index cases with fever was 43% higher than for secondary cases. The proportion of symptomatic, hospitalized, and fatal illnesses among index cases were 12%, 126%, and 179% higher than for secondary cases, respectively. We developed a statistical model to utilize the severity difference, and estimate 55% of index cases were missed in Wuhan, China. Information on disease severity in secondary cases should be less susceptible to ascertainment bias and could inform estimates of disease severity and the proportion of missed index cases.

Suggested Citation

  • Tim K. Tsang & Can Wang & Bingyi Yang & Simon Cauchemez & Benjamin J. Cowling, 2021. "Using secondary cases to characterize the severity of an emerging or re-emerging infection," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26709-7
    DOI: 10.1038/s41467-021-26709-7
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    References listed on IDEAS

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    2. Mobina Fathi & Kimia Vakili & Fatemeh Sayehmiri & Ashraf Mohamadkhani & Mohammadreza Hajiesmaeili & Mostafa Rezaei-Tavirani & Owrang Eilami, 2021. "The prognostic value of comorbidity for the severity of COVID-19: A systematic review and meta-analysis study," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-25, February.
    3. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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    Cited by:

    1. Thomas Ward & Christopher E. Overton & Robert S. Paton & Rachel Christie & Fergus Cumming & Martyn Fyles, 2024. "Understanding the infection severity and epidemiological characteristics of mpox in the UK," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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