×

When to lift the lockdown in Hubei province during COVID-19 epidemic? An insight from a patch model and multiple source data. (English) Zbl 1455.92142

Summary: After diagnosed in Wuhan, COVID-19 spread quickly in mainland China. Though the epidemic in regions outside Hubei in mainland China has maintained a degree of control, evaluating the effectiveness and timeliness of intervention strategies, and predicting the transmission risk of work resumption as well as lifting the lockdown in Hubei province remain urgent. A patch model reflecting the mobility of population between Hubei and regions outside Hubei is formulated, and parameterized based on multiple source data for Hubei and regions outside Hubei. The effective reproduction numbers for Hubei and regions outside Hubei are estimated as 3.59 and 3.26 before Jan 23rd, 2020, but decrease quickly since then and drop below 1 after Jan 31st and Jan 28th, 2020. It is predicted that the new infections in Hubei province will decrease to very low level in mid-March, and the final size is estimated to be about 68,500 cases. The simulations reveal that contact rate after work resumption or lifting the lockdown in Hubei plays a critical role in affecting the epidemic. If the contact rate could be kept at a relatively low level, work resumption starting as early as on March 2nd in Hubei province may not induce the secondary outbreak, and the daily new infectious cases can be controlled at a low level if the lockdown in Hubei is lifted after March 9th, otherwise both work resumption and lifting the lockdown in Hubei should be postponed.

MSC:

92D30 Epidemiology
PDFBibTeX XMLCite
Full Text: DOI Link

References:

[1] Baidu migration. URL:http://qianxi.baidu.com/.
[2] Cao, Z., Zhang, Q., Lu, X., et al., 2020. Incorporating human movement data to improve epidemiological estimates for 2019-nCoV. medRxiv.
[3] Diekmann, O.; Heesterbeek, J. A.P.; Roberts, M. G., The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface, 7, 47, 873-885 (2010)
[4] Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention, 2020. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi. 41(2), 145-151.
[5] Health Commission of Hubei Province. URL:http://wjw.hubei.gov.cn/.
[6] Jung, S.; Akhmetzhanov, A. R.; Hayashi, K., Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases, J. Clin. Med., 9, 2, 523 (2020)
[7] King, A. A.; Domenech de Cellès, M.; Magpantay, F. M.; Rohani, P., Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola, Proc. Biol. Sci., 282, 1806, 20150347 (2015)
[8] Li, Q.; Guan, X.; Wu, P., Early transmission dynamics in Wuhan, China, of novel coronavirus C infected pneumonia, New. Engl. J. Med., 382, 13, 1199-1207 (2020)
[9] Liu, T., Hu, J., Kang, M., et al., 2020. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv.
[10] National Bureau of Statistics of China. China Statistical Yearbook 2019. China Statistics Press, Beijing 2019.
[11] National Health Commission of the People’s Republic of China. URL:http://www.nhc.gov.cn/.
[12] Read, J.M., Bridgen, J.R.E., Cummings, D.A.T., et al., 2020. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. medRxiv.
[13] Riou, J.; Althaus, C. L., Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020, Euro. Surveill., 25, 4, 2000058 (2020)
[14] Sanche, S.; Lin, Y.; Xu, C., High contagiousness and rapid spread of severe acute respiratory syndrome coronavirus 2, Emerg. Infect. Dis., 26, 7, 1470-1477 (2020)
[15] Shen, M., Peng, Z., Xiao, Y., et al., 2020. Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China. bioRxiv.
[16] Tang, B.; Wang, X.; Li, Q., Estimation of the transmission risk of the 2019-ncov and its implication for public health interventions, J. Clin. Med., 9, 2, 462 (2020)
[17] Tang, B.; Bragazzi, N. L.; Li, Q., An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov), Infect. Dis. Model., 5, 248-255 (2020)
[18] Tang, B.; Xia, F.; Tang, S., The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China, Int. J. Infect. Dis., 95, 288-293 (2020)
[19] Van den Driessche, P.; Watmough, J., Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180, 1-2, 29-48 (2002) · Zbl 1015.92036
[20] Wang, X.; Tang, S.; Chen, Y., When will be the resumption of work in Wuhan and its surrounding areas during COVID-19 epidemic? A data-driven network modeling analysis (in Chinese), Sci. Sin. Math., 50, 1-10 (2020) · Zbl 1499.34288
[21] Wu, J. T.; Leung, K.; Leung, G. M., Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395, 10225, 689-697 (2020)
[22] Zhao, S.; Lin, Q.; Ran, J., Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak, Int. J. Infect. Dis., 92, 214-217 (2020)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.