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Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data.
Zhao, Shi; Tang, Biao; Musa, Salihu S; Ma, Shujuan; Zhang, Jiayue; Zeng, Minyan; Yun, Qingping; Guo, Wei; Zheng, Yixiang; Yang, Zuyao; Peng, Zhihang; Chong, Marc Kc; Javanbakht, Mohammad; He, Daihai; Wang, Maggie H.
  • Zhao S; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: zhaoshi.cmsa@gmail.com.
  • Tang B; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada. Electronic address: btang66@yorku.ca.
  • Musa SS; Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria. Electronic address: salihu-sabiu.musa@connect.polyu.hk.
  • Ma S; Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China. Electronic address: 790039940@qq.com.
  • Zhang J; Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China. Electronic address: zhangjiayue@csu.edu.cn.
  • Zeng M; Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China. Electronic address: zmy6691@gmail.com.
  • Yun Q; Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China. Electronic address: yunqingping@bjmu.edu.cn.
  • Guo W; Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China. Electronic address: 176912045@csu.edu.cn.
  • Zheng Y; Department of Infectious Diseases, Key Laboratory of Viral Hepatitis of Hunan, Xiangya Hospital, Central South University, Changsha, China. Electronic address: yxzheng@csu.edu.cn.
  • Yang Z; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. Electronic address: zyang@cuhk.edu.hk.
  • Peng Z; Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China. Electronic address: zhihangpeng@njmu.edu.cn.
  • Chong MK; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: marc@cuhk.edu.hk.
  • Javanbakht M; Nephrology and Urology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. Electronic address: mhmjvbt81@gmail.com.
  • He D; Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: daihai.he@polyu.edu.hk.
  • Wang MH; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: maggiew@cuhk.edu.hk.
Epidemics ; 36: 100482, 2021 09.
Article Dans Anglais | MEDLINE | ID: covidwho-1281413
ABSTRACT
The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI 6.2, 7.5) and SD at 4.1 days (95 %CI 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI 5.4, 7.6) and SD at 1.8 days (95 %CI 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.
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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Traçage des contacts / COVID-19 Limites du sujet: Humains langue: Anglais Revue: Epidemics Année: 2021 Type de document: Article

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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Traçage des contacts / COVID-19 Limites du sujet: Humains langue: Anglais Revue: Epidemics Année: 2021 Type de document: Article