The outbreak of New Coronary Pneumonia COVID-19 and its spread throughout the China and many foreign countries have produced a huge social impact. Studying the dynamic characteristics of epidemic transmission will help us better control and prevent the epidemic. We have developed a discrete stochastic method to simulate the evolvement of the epidemic in Hubei Province of China. Firstly, the daily confirmed number of patients was processed according to the Erlang probability distribution of the queuing theory, and the daily number of patients onset and infected were obtained. The results are compared with references recently published by Chinese CDC, validating the scientific credibility of the method. Then, the effective reproduction rates at different stages of the epidemic are inverted to fit the number of daily onsets, and predict the future trend of the epidemic. It was found that the basic reproduction number R0 decreased from 6.1 to 4.0 at the initial stage of the epidemic. After taking dramatic measures to close the Wuhan city, the effective R value decreased below 1, and gradually decreased to below 0.13. The peak of the onset of patients has already passed in early February. Although there small fluctuations in the epidemic are not ruled out, the overall trend will not change as long as strict quarantine measures are adhered to. The epidemic is expected to end around the end of March and the cumulative number of patients will reach around 71,000. Migrant workers and students returning after the Spring Festival are unlikely to induce a large epidemic rebound. However, some countries in the world are at the stage of possible outbreaks, and China should pay attention to inspecting and quarantine international travelers.
[1] 陈锡康, 杨翠红, 鲍勤, 等. 新冠肺炎疫情对中国经济的影响分析与对策建议[J]. 中国科学院院刊, 2020, 35. DOI:10.16418/j.issn.1000-3045.20200210003.
[2] Boldog P, Tekeli T, Vizi Z, et al. Risk assessment of novel coronavirus COVID-19 outbreaks outside China[J]. Journal of Clinical Medicine, 2020, 9(2):571.
[3] Adhikari S P, Meng S, Wu Y., et al. A literature review of 2019 novel coronavirus during the early outbreak period:epidemiology, causes, clinical manifestation and diagnosis, prevention and control[J]. Preprints (www.preprints.org). Distributed under a Creative Commons CC BY license, 2020, 20200206:1-12.
[4] Heymann D L, Shindo N. COVID-19:what is next for public health?[J]. The Lancet, 2020, 395(10224):542-545. DOI:https://doi.org/10.1016/S0140-6736(20)30374-3.
[5] Danon L, Brooks-Pollock E, Bailey M, et al. A spatial model of CoVID-19 transmission in England and Wales:early spread and peak timing[J/OL]. (2020-02-12)[2020-02-25].Available under a CC-BY-NC-ND 4.0 International license. https://doi.org/10.1101/2020.02.12.20022566.
[6] Li Q, Feng W. Trend and forecasting of the COVID-19 outbreak in China[J]. arXiv preprint, 2002.05866, 2020.
[7] 赵序茅, 李欣海, 聂常虹, 等. 基于大数据回溯新冠肺炎的扩散趋势及中国对疫情的控制研究[J]. 中国科学院院刊,2020, 35. doi:10.16418/j.issn.1000-3045.20200210002.
[8] 许小可, 文成, 张光耀, 等. 新冠肺炎爆发前期武汉外流人口的地理去向分布及影响[J]. 电子科技大学学报, 2020, 35. doi:10.12178/1001-0548.2020033.
[9] Chen B, Shi M, Ni X, et al. Data Visualization Analysis and Simulation Prediction for COVID-19[J]. arXiv preprint:2002.07096, 2020.
[10] 喻孜, 张贵清, 刘庆珍, 等. 基于时变参数-SIR模型的2019-nCoV疫情评估和预测[J]. 电子科技大学学报, 2020, 35. doi:10.12178/1001-0548.2020027.
[11] Du Z, Wang L, Cauchemez S, et al. Risk for Transportation of 2019 Novel Coronavirus (COVID-19) from Wuhan to Cities in China[J/OL]. (2020-01-28)[2020-02-25]. Available under a CC-BY-NC-ND 4.0 International license. medRxiv, 2020. https://doi.org/10.1101/2020.01.28.20019299.
[12] Luo W, Majumder M S, Liu D, et al. The role of absolute humidity on transmission rates of the COVID-19 outbreak[J/OL]. (2020-02-12)[2020-02-25]. Available under a CC-BY-NC-ND 4.0 International license. medRxiv, 2020. https://doi.org/10.1101/2020.02.12.20022467
[13] Anderson R M, May R M. Infectious diseases of humans:dynamics and control[M], Oxford:Oxford Univ. Press, 1991.
[14] Shi Y L. Stcochastic dynamic model of SARS spreading[J]. Chinese Science Bulletin. 2003, 48(13):1287-1292. 石耀霖. SARS传播的动力学模型[J]. 科学通报, 2003, 48(13):1373-1378.
[15] Ball F G, Barbour A D. Poisson approximation for some epidemic models[J]. Journal of Applied Probability, 1990, 27:479-490.
[16] Lefkvre C, Utev S. Mixed Poisson approximation in the collective epidemic model[J]. Stochastic Processes and their Applications, 1997, 69:217246.
[17] Bogaards J A, Putter H, Weverling G J, et al. The potential of targeted antibody prophylaxis in SARS outbreak control:a mathematic analysis[J]. Travel Medicine and Infectious Disease, 2007, 5:70-78.
[18] Ridenhour B, Jessica M K, Shay D K. Unraveling R0:considerations for public health applications[J]. American Journal of Public Health, 2014, 104(2):32-41
[19] Frank B, Barbour A D. Poisson approximation for some epidemic models[J]. Journal of Applied Probability,1990, 27(3):479-490.
[20] Lipsitch M, Riley S, Cauchemez S, et al. Ferguson, managing and reducing uncertainty in an emerging influenza pandemic[J]. The New England Journal of Medicine, 2009; 361:112-115. DOI:10.1056/NEJMp0904380.
[21] Champredon D, Dushoff J, Earn D. Equivalence of the Erlang-distributed SEIR epidemic model and the renewal equation[J]. SIAM Journal on Applied Mathematics, 2018, 78(6):3258-3278. doi:10.1137/18M1186411.
[22] Krylova O, Earn D J D. Effects of the infectious period distribution on predicted transitions in childhood disease dynamics[J]. Jouranal of the Royal Socity Interface, 2013, 10(84):20130098. doi:10.1098/rsif.2013.0098.
[23] Masè M, Faes L, Nollo G, et al. Determination of synchronization of electrical activity in the heart by Shannon entropy measure[C]//Proceedings of the First International Meeting on Applied Physics (APHYS-2003). October 13-18.2003. Badajoz, Spain. Recent Advances in Multidisciplinary Applied Physics, Elsevier. Mendezvilas Amsterdam, 2005.235-239. https://doi.org/10.1016/B978-008044648-6.50038-0.
[24] 中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组. 新型冠状病毒肺炎流行病学特征分析[J]. 中华流行病学杂志, 2020, 41(2):145-151.
[25] Backer J A, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020[J]. Euro Surveillance,[2020-02-25]. https://doi.org/10.2807/1560-7917.ES.2020.25.5.2000062.
[26] Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia[J]. The New England Journal of Medicine, January 29, 2020, doi:10.1056/NEJMoa2001316.
[27] Guan W, Ni Z, Hu Y, et al. Clinical characteristics of 2019 novel coronavirus infection in China[J]. New England Journal of Medicine, 2020. doi:10.1056/NEJMoa2002032.
[28] Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J/OL]. www.thelancet.com.(2020-01-24)[2020-02-25].https://doi.org/10.1016/S0140-6736(20)30183-5.
[29] Report of the WHO-China joint mission on coronavirus disease 2019(COVID-19)[R/OL].(2020-02-28)[2020-03-03]. https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.
[30] You C, Deng Y, Hu W, et al. Estimation of the time-varying reproduction number of COVID-19 outbreak in China[J]. medRxiv, 2020.https://doi.org/10.1101/2020.02.08.20021253.
[31] Obadia T, Haneef R, Boëlle P Y. The R0 package:a toolbox to estimate reproduction numbers for epidemic outbreaks[J]. BMC Medical Informatics and Decision Making, 2012, 12(1):147.
[32] Wu J, 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[J/OL]. Lancet.[2020-03-03]. https://doi.org/10.1016/S0140-6736(20)30260-9.
[33] Zhao S, Lin Q, Ran J, et al. 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[J]. International Journal of Infectious Diseases, 2020, 92:214-217. doi:https://doi.org/10.1016/j.ijid.2020.01.050.
[34] Jung S-M, Akhmetzhanov A R, Hayashi K, et al. Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection:inference using exported cases[J/OL]. Clinical Medicine. 2020, 9, 523[2020-02-24].https://doi.org/10.3390/jcm9020523.
[35] Liu T, Hu J, Kang M, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV)[J/OL]. bioRxiv 2020.(2020-01-25)[2020-02-24]. https://doi.org/10.1101/2020.01.25.919787.
