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Abstract
An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6, and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077–84,520 or 55,869–81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.
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1 Shanghai Jiao Tong University School of Medicine, School of Public Health, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)
2 Shanghai Cancer Center, Fudan University, Department of Cancer Prevention, Shanghai, China (GRID:grid.16821.3c); Shanghai Medical College, Fudan University, Department of Oncology, Shanghai, China (GRID:grid.11841.3d) (ISNI:0000 0004 0619 8943)
3 China Medical University, Department of Environmental and Occupational Health, School of Public Health, Shenyang, China (GRID:grid.412449.e) (ISNI:0000 0000 9678 1884)
4 Sanlin Community Health Service Center, Shanghai, China (GRID:grid.16821.3c)
5 Shanghai Jiao Tong University School of Medicine, State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine (Shanghai), Rui-Jin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)
6 Shanghai Jiao Tong University School of Medicine, Research Laboratory of Clinical Virology, National Research Center for Translational Medicine (Shanghai), Rui-Jin Hospital, and Rui-Jin Hospital North, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)