Research article Special Issues

Impact of household quarantine on SARS-Cov-2 infection in mainland China: A mean-field modelling approach

  • Received: 22 May 2020 Accepted: 19 June 2020 Published: 23 June 2020
  • The novel coronavirus, named SARS-Cov-2, has raged in mainland China for more than three months, and it causes a huge threat to people's health and economic development. In order to curb the SARS-Cov-2 prevalence, the Chinese government enacted a series of containment strategies including household quarantine, traffic restriction, city lockdowns etc. Indeed, the pandemic has been effectively mitigated, but the global transmission is not still optimistic. Evaluating such control measures in detail plays an important role in limiting SARS-Cov-2 spread for public health decision and policymakers. In this paper, based on the cumulative numbers of confirmed cases and deaths of SARS-Cov-2 infection, from January 31st to March 31st, announced by the National Health Commission of the People's Republic of China, we established a mean-field model, considering the substantial contact change under some restrictive measures, to study the dynamics of SARS-Cov-2 infection in mainland China. By the Metropolis-Hastings (M-H) algorithm of Markov Chain Monte Carlo numerical method, our model provided a good fitting to the overall trends of SARS-Cov-2 infections and discovers the transmission heterogeneities by some extreme containment strategies to some extent. The basic reproduction number was approximated to be 2.05 (95% CI [1.35, 2.87]); the hospitalized cases arrived at the peak of 29766 (95% CI [29743, 29868]) on February 7th (95% CI [Feb.6th, Feb.8th]). Importantly, we identified that the highest risk group of SARS-Cov-2 was the family of four, which has the biggest probability of degree distributions at such node, suggesting that contact patterns play an important role in curtailing the disease spread.

    Citation: Junyuan Yang, Guoqiang Wang, Shuo Zhang. Impact of household quarantine on SARS-Cov-2 infection in mainland China: A mean-field modelling approach[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4500-4512. doi: 10.3934/mbe.2020248

    Related Papers:

  • The novel coronavirus, named SARS-Cov-2, has raged in mainland China for more than three months, and it causes a huge threat to people's health and economic development. In order to curb the SARS-Cov-2 prevalence, the Chinese government enacted a series of containment strategies including household quarantine, traffic restriction, city lockdowns etc. Indeed, the pandemic has been effectively mitigated, but the global transmission is not still optimistic. Evaluating such control measures in detail plays an important role in limiting SARS-Cov-2 spread for public health decision and policymakers. In this paper, based on the cumulative numbers of confirmed cases and deaths of SARS-Cov-2 infection, from January 31st to March 31st, announced by the National Health Commission of the People's Republic of China, we established a mean-field model, considering the substantial contact change under some restrictive measures, to study the dynamics of SARS-Cov-2 infection in mainland China. By the Metropolis-Hastings (M-H) algorithm of Markov Chain Monte Carlo numerical method, our model provided a good fitting to the overall trends of SARS-Cov-2 infections and discovers the transmission heterogeneities by some extreme containment strategies to some extent. The basic reproduction number was approximated to be 2.05 (95% CI [1.35, 2.87]); the hospitalized cases arrived at the peak of 29766 (95% CI [29743, 29868]) on February 7th (95% CI [Feb.6th, Feb.8th]). Importantly, we identified that the highest risk group of SARS-Cov-2 was the family of four, which has the biggest probability of degree distributions at such node, suggesting that contact patterns play an important role in curtailing the disease spread.


    加载中


    [1] World Health Organization (WHO), 2020. Coronavirus. Available from: https://www.who.int/healthtopics/coronavirus.
    [2] The National Health Comision of the People's Republic of China, 2020. Available from: http://www.nhc.gov.cn/xcs/yqtb/listgzbd.shtml.
    [3] Chinese Control Disease Center and Prevention, 2020. Available from: http://www.nhc.gov.cn/xcs/yqtb/listgzbd.shtml.
    [4] The platform of 2019-nCov infection, 2020. Available from: http://www.clas.ac.cn/xwzx2016/163486/xxfysjpt2020/.
    [5] H. Tian, Y. Liu, Y. Li, C. Wu, B. Chen, M. Kraemer, et al., An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China, Science, 368 (2020), 638- 642.
    [6] M. Chinazz, J.T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler, et al., The effect of travel restriction on the spread of the 2019 novel coronavirus (COVID-19) outbreak, Science, 368 (2020), 395-400.
    [7] C. R. Wells, P. Sah, S. M. Moghadas, A. Pandey, A. Shoukat, Y. Wang, et al., Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak, Natl. Acad. Sci. USA, 117 (2020), 7504-7509.
    [8] J. Zhang, M. Litvinova, Y. Liang, Y. Wang, W. Wang, S. Zhao, et al., Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China, Science, (2020), doi:10.1126/science.abb8001.
    [9] X. Luo, S. Feng, J. Yang, X. Peng, X. Cao, J. Zhang, et al., Analysis of potential risk of COVID-19 infections in China based on a pairwise epidemic model, 2020. Available from: https://www.preprints.org/manuscript/202002.0398/v1.
    [10] B. J. Cowling, G.M. Leung, Epidemiological research priorities for public health control of the ongoing global novel coronavirus (2019-nCoV) outbreak, Euro Surveill., 25 (2020), 2000110.
    [11] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early transmission dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia, N. Engl. J. Med., 382 (2020), 1199-1207.
    [12] P. Shao, Y. G. Shan, Beware of asymptomatic transmission: study on 2019-nCov preventtion and control measures based on SEIR model, 2020. https://doi.org/10.1101/2020.01.28.923169.
    [13] S. Zhao, Q. Lin, J. Ran, S. Musa, G. Yang, W. Wang, et al., Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A datadriven analysis in the early phase of the outbreak, Int. J. Infect. Dis., 92 (2020), 214-217.
    [14] B. Tang, X. Wang, Q. Li, N. Bragazzi, S. Tang, Y. Xiao, et al., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions, J. Clin. Med., 9 (2020), 462.
    [15] X. Wang, Z. Ma, Y. Ning, C. Chen, R. Chen, Q. Chen, et al., Estimating the case fatality ratio of the COVID-19 epidemic in China, medRxiv, 2020. http://dx.doi.org/10.1101/2020.02.12.20022434.
    [16] Chinese Population Statistic Yearbook 2019, 2020. Available from: http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm.
    [17] J. Yang, F. Xu, The computational approach for the basic reproduction number of epidemic models on complex networks, IEEE Access, 7 (2019), 26474-26479.
    [18] P. Spychalski, A. Blazynska-Spychalska, J. Kobiela, Estimating case fatality rates of COVID- 19, Lancet Infect. Dis., 2020. Available from: https://doi.org/10.1016/S1473-3099(20)30246-2.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3611) PDF downloads(356) Cited by(7)

Article outline

Figures and Tables

Figures(4)  /  Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog