Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation.
Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation.
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通过时变传染数估计分析中国主要城市COVID-19防控的异质性和有效性。
DOI：
10.1038/s41598-020-79063-x
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发表时间：
2020-12-15
影响因子：
4.6
通讯作者：
Huang J
中科院分区：
文献类型：
--
作者：
Cheng Q;Liu Z;Cheng G;Huang J
从2019年12月31日开始，中国出现大规模新型冠状病毒病2019 （COVID-19）。跟踪分析城市新冠肺炎疫情防控的异质性和有效性，对于制定和调整疫情防控措施至关重要。收集1月11日至2月10日中国25个新冠肺炎疫情最严重城市的新确诊病例数。采用估计时变再现数法和序列相关法分析25个城市新冠肺炎防控措施的异质性和有效性。结果表明，25个城市的有效繁殖数(R)总体呈下降趋势，但城市间R变化趋势差异显著，说明新冠肺炎在城市传播和控制存在异质性。25个城市中有21个防控有效，截至2020年2月10日，R值降至1以下，感染风险下降。武汉、天门、鄂州、恩施4个城市的R值均大于1，短期内仍难以有效控制疫情。
Beginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.
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影响因子：
158.5
作者：
Li, Qun;Guan, Xuhua;Feng, Zijian
通讯作者：
Feng, Zijian
DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
16.6
作者：
Wang L;Wu JT
通讯作者：
Wu JT
根据发病数据实时估算2020年中国新型冠状病毒病（COVID-19）的传染数
影响因子：
--
作者：
Wang, Kai;Zhao, Shi;He, Daihai
通讯作者：
He, Daihai
典型国家COVID-19繁殖数及不同防控场景下疫情趋势估算
影响因子：
8.1
作者：
Xu, Chen;Dong, Yinqiao;Cai, Yong
通讯作者：
Cai, Yong