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Using Secondary Cases to Characterize the Severity of an Emerging or Re-Emerging Infection
35 Pages Posted: 27 May 2021
More...Abstract
Background: The methods to ascertain cases of an emerging infectious disease are typically biased toward cases with more severe disease, which can affect estimates of the average infection-severity profile. We examine the potential for using data from secondary cases, identified through transmission studies or contact tracing of index cases, to characterize disease severity.
Methods: We extracted information on reported symptoms, disease severity and fatality risk among index cases and secondary cases, from previous reviews of contact tracing studies for pandemic influenza A(H1N1)pdm09, Middle East Respiratory Syndrome (MERS) and Coronavirus Disease 2019 (COVID-19). We compared severity profiles between index cases and secondary cases and inferred the potential for ascertainment bias in confirmed cases.Findings. Overall, index cases had more severe illness on average than secondary cases, for each disease. For COVID-19 and influenza A(H1N1)pdm09, the proportions of index cases with fever and cough were 1.3-fold to 1.6-fold higher than for secondary cases. For COVID-19, the proportion of index cases with asymptomatic infection, severe/critical illness and death were 54% lower, 39% higher and 82% higher than for secondary cases, respectively. For MERS, the fatality risk among index cases was 73% higher than for secondary cases. For COVID-19 in China, we estimated that 68% (95% Credible interval (CrI): 43%, 85%) and 56% (95% CrI: 42%, 68%) of index cases were missed due to ascertainment bias, for Guangzhou and Wuhan, respectively.
Interpretation: 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.
Funding: This project was supported by the Health and Medical Research Fund, Food and
Health Bureau, Government of the Hong Kong Special Administrative Region (grant no. COVID190118) and the Collaborative Research Fund (Project No. C7123-20G) of the Research Grants Council of the Hong Kong SAR Government.BJC is supported by the AIR@innoHK program of the Innovation and Technology Commission of the Hong Kong SAR Government.
Declaration of Interest: BJC reports honoraria from Sanofi Pasteur, GSK, Moderna and Roche. The
authors report no other potential conflicts of interest.
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