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ABSTRACT: Background
Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics.Methods
A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January-29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions.Results
There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4-5.5) days and 5.2 (95% CrI, 4.9-5.7) days for household transmissions and 5.2 (95% CrI, 4.6-5.8) and 5.3 (95% CrI, 4.9-5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18-64 years, whereas hazard of being infected within households is higher for young and old people.Conclusions
Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.
SUBMITTER: Xu XK
PROVIDER: S-EPMC7337632 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20201201 12
<h4>Background</h4>Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics.<h4>Methods</h4>A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstruct ...[more]