| 王莹,尤心怡,王奕婧,彭丽萍,杜志成,Stuart Gilmour,Daisuke Yoneoka,顾菁,郝春,郝元涛,李菁华.中国新型冠状病毒肺炎疫情基本再生数评估[J].Chinese journal of Epidemiology,2020,41(4):476-479 |
| 中国新型冠状病毒肺炎疫情基本再生数评估 |
| Estimating the basic reproduction number of COVID-19 in Wuhan, China |
| Received:February 10, 2010 |
| DOI：10.3760/cma.j.cn112338-20200210-00086 |
| KeyWord: 新型冠状病毒肺炎 基本再生数 传播速率 |
| English Key Word: COVID-19 Basic reproduction number Transmission rate |
| FundProject:国家自然科学基金（81803334，71774178，71974212，81973150）；美国中华医学基金会（18-301）；国家科技重大专项（2018ZX10715004）；广东省省级科技计划（2017A020212006）；广州市科学（技术）研究专项（201607010331，20160701368） |
| Author Name | Affiliation | E-mail | | Wang Ying | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China | | | You Xinyi | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China | | | Wang Yijing | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Graduate School of Public Health, St. Luke's International University, Tokyo 104-0045, Japan | | | Peng Liping | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China | | | Du Zhicheng | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China | | | Stuart Gilmour | Graduate School of Public Health, St. Luke's International University, Tokyo 104-0045, Japan | | | Daisuke Yoneoka | Graduate School of Public Health, St. Luke's International University, Tokyo 104-0045, Japan | | | Gu Jing | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China | | | Hao Chun | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China | | | Hao Yuantao | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China | haoyt@mail.sysu.edu.cn | | Li Jinghua | School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China | lijinghua3@mail.sysu.edu.cn |
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| Abstract: |
| 目的 目前湖北省的新型冠状病毒肺炎(COVID-19)确诊和疑似病例的数量仍在增加。国内外多个团队对疫情发展进行了模型预测,但结论并不统一。因此,开展本次疫情的预测模型研究、评估COVID-19的基本再生数(basic reproduction number,R0),对于评估病毒的传播能力以及一系列控制措施的效果具有重要意义。方法 收集从湖北省2020年1月17日到2月8日期间每天报告的确诊病例数等数据,分别采用指数增长方法(exponential growth,EG)、极大似然法(maximum likelihood estimation,ML)、序贯贝叶斯方法(sequential Bayesian method,SB)和时间相关基本再生数(time dependent reproduction numbers,TD)估计R0值。结果 由观测病例数和4种方法预测的病例数的拟合情况可知,EG方法拟合效果最优。EG方法估计COVID-19湖北省R0的值为3.49(95%CI:3.42~3.58)。采取封城控制手段期间,EG方法估算R0值为2.95(95%CI:2.86~3.03)。结论 在传染病流行初期,适合采用EG方法估算R0。同时需要采取及时有效的控制措施,进一步降低COVID-19的传播速率。 |
| English Abstract: |
| Objective The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (R0) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. Methods The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R0. Results Among the four methods, the EG method fitted the data best. The estimated R0 was 3.49 (95%CI:3.42-3.58) by using EG method. The R0 was estimated to be 2.95 (95%CI:2.86-3.03) after taking control measures. Conclusions In the early stage of the epidemic, it is appropriate to estimate R0 using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19. |
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