Journal article

Early characteristics of the COVID-19 outbreak predict the subsequent epidemic scope

Lei Zhang, Yusha Tao, Jing Wang, Jason J Ong, Weiming Tang, Maosheng Zou, Lu Bai, Miao Ding, Mingwang Shen, Guihua Zhuang, Christopher K Fairley

International Journal of Infectious Diseases | ELSEVIER SCI LTD | Published : 2020

Abstract

OBJECTIVES: The mostly-resolved first wave of the COVID-19 epidemic in China provided a unique opportunity to investigate how the initial characteristics of the COVID-19 outbreak predict its subsequent magnitude. METHODS: We collected publicly available COVID-19 epidemiological data from 436 Chinese cities from 16th January-15th March 2020. Based on 45 cities that reported >100 confirmed cases, we examined the correlation between early-stage epidemic characteristics and subsequent epidemic magnitude. RESULTS: We identified a transition point from a slow- to a fast-growing phase for COVID-19 at 5.5 (95% CI, 4.6-6.4) days after the first report, and 30 confirmed cases marked a critical thresho..

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University of Melbourne Researchers

Grants

Awarded by National Natural Science Foundation of China


Awarded by Outstanding Young Scholars Funding


Awarded by Xi'an Jiaotong University Basic Research and Profession Grant


Awarded by Xi'an Jiaotong University Young Talent Support Grant


Awarded by China Postdoctoral Science Foundation


Awarded by Fundamental Research Funds for the Central Universities


Awarded by Natural Science Basic Research Program of Shaanxi Province


Awarded by Xi'an Special Science and Technology Projects on Prevention and Treatment of Novel Coronavirus Pneumonia Emergency


Funding Acknowledgements

The work was supported by the Bill & Melinda Gates Foundation. Lei Zhang is supported by the National Natural Science Foundation of China (Grant number: 81950410639); Outstanding Young Scholars Funding (Grant number: 3111500001); Xi'an Jiaotong University Basic Research and Profession Grant (Grant number: xtr022019003 and xzy032020032) and Xi'an Jiaotong University Young Talent Support Grant (Grant number: YX6J004). Mingwang Shen was supported by the National Natural Science Foundation of China [grant number: 11801435 (MS)], China Postdoctoral Science Foundation [grant number: 2018M631134]; the Fundamental Research Funds for the Central Universities [grant number: xjh012019055, xzy032020026]; Natural Science Basic Research Program of Shaanxi Province [Grant number: 2019JQ187]; and Xi'an Special Science and Technology Projects on Prevention and Treatment of Novel Coronavirus Pneumonia Emergency [Grant number: 20200005YX005]. We are thankful for the input and discussion from Guoqiang Li and Xinghui Li.