1. Using secondary cases to characterize the severity of an emerging or re-emerging infection
- Author
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Can Wang, Benjamin J. Cowling, Bingyi Yang, Tim K. Tsang, Simon Cauchemez, Li Kashing Faculty of Medicine, The University of Hong Kong (HKU), Hong Kong Science and Technology Parks Corporation (HKSTP), Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), 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, B.J.C.) and the Collaborative Research Fund (Project No. C7123-20G, B.J.C.) 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., and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Index (economics) ,Statistical methods ,Epidemiology ,General Physics and Astronomy ,Disease ,MESH: Hospitalization ,Severity of Illness Index ,0302 clinical medicine ,Credible interval ,MESH: COVID-19 ,030212 general & internal medicine ,050207 economics ,[MATH]Mathematics [math] ,Sampling bias ,0303 health sciences ,050208 finance ,Multidisciplinary ,05 social sciences ,MESH: China ,3. Good health ,Hospitalization ,Emerging infectious disease ,medicine.symptom ,China ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Science ,MEDLINE ,macromolecular substances ,Asymptomatic ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Disease severity ,MESH: Severity of Illness Index ,Internal medicine ,0502 economics and business ,Severity of illness ,medicine ,Humans ,030304 developmental biology ,Models, Statistical ,MESH: Humans ,SARS-CoV-2 ,business.industry ,COVID-19 ,General Chemistry ,medicine.disease ,Risk factors ,Middle East respiratory syndrome ,business ,MESH: Models, Statistical ,Contact tracing ,Demography - 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., Estimates of the severity of emerging infections did not consider the case ascertainment method, but secondary cases identified by contact tracing of index cases may be more reliable as they are less susceptible to ascertainment bias. Here, the authors perform a systematic review to quantify these differences and model their impacts for COVID-19.
- Published
- 2021
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