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 andHealth 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. Theauthors report no other potential conflicts of interest.
Highlights
To cite this version: Tim Tsang, Can Wang, Bingyi Yang, Simon Cauchemez, Benjamin Cowling
We assessed how the presence of different types of symptoms, case severity, symptom status, hospitalization status and fatality status could differ between index and secondary cases in contact tracing studies for COVID-19
For these measures of severity, we found that index cases were generally more severe secondary cases
Summary
To cite this version: Tim Tsang, Can Wang, Bingyi Yang, Simon Cauchemez, Benjamin Cowling. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. 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. The proportion of index cases with fever was 43% higher than for secondary cases. 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. While reasonable estimates of transmissibility can usually be inferred from growth rates in identified cases[1,4], it can be more challenging to obtain accurate initial estimates of the clinical severity of infections. Estimates of severity of new infectious diseases need to take into account the potential for ascertainment bias and censoring of outcomes[6,7,8,9,10,11]
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