| 陈佳丽,金美玲,王霞,杨晓静,张娜,刘芳妮,刘嵘,郭金鹏,陈勇,王长军.基于SVEPIUHDR传染病动力学模型的新型冠状病毒肺炎疫情趋势分析[J].Chinese journal of Epidemiology,2021,42(8):1341-1346 |
| 基于SVEPIUHDR传染病动力学模型的新型冠状病毒肺炎疫情趋势分析 |
| Fitting and predicting trend of COVID-19 by SVEPIUHDR dynamic model |
| Received:February 25, 2021 |
| DOI：10.3760/cma.j.cn112338-20210225-00147 |
| KeyWord: 新型冠状病毒肺炎 传播动力学 预测 疫苗 |
| English Key Word: COVID-19 Transmission dynamics Forecast Vaccine |
| FundProject:国家重点研发计划（2019YFC1200501）；北京市科技新星计划（Z181100006218107） |
| Author Name | Affiliation | E-mail | | Chen Jiali | School of Public Health, China Medical University, Shenyang 110122, China | | | Jin Meiling | School of Public Health, China Medical University, Shenyang 110122, China | | | Wang Xia | Shaanxi Normal University, Xi'an 710119, China | | | Yang Xiaojing | School of Public Health, China Medical University, Shenyang 110122, China | | | Zhang Na | School of Public Health, China Medical University, Shenyang 110122, China | | | Liu Fangni | School of Public Health, China Medical University, Shenyang 110122, China | | | Liu Rong | School of Public Health, China Medical University, Shenyang 110122, China | | | Guo Jinpeng | Chinese People's Liberation Army Center for Disease Control and Prevention, Beijing 100071, China | | | Chen Yong | Chinese People's Liberation Army Center for Disease Control and Prevention, Beijing 100071, China | chenyonger@126.com | | Wang Changjun | Chinese People's Liberation Army Center for Disease Control and Prevention, Beijing 100071, China | |
|
| Hits: 3366 |
| Download times: 1177 |
| Abstract: |
| 目的 对美国和英国新型冠状病毒肺炎（COVID-19）疫情的发展趋势进行模型拟合和预测，对疫苗接种的效果进行初步分析。方法 在SEIR模型基础上，增加症状前感染者、隔离措施及疫苗接种等要素，建立SVEPIUHDR模型。利用公开发布的数据建模，分别将美国2020年11月6日至2021年1月31日和英国2020年11月23日至2021年1月31日的数据进行拟合，2021年2月1日至4月1日的疫情数据评估预测效果，使用R 4.0.3软件进行分析，并预测在疫苗不同接种率下每日新增病例数的变化。结果 SVEPIUHDR模型对美国和英国的累计确诊病例数的拟合及预测平均偏差均<5%。按计划接种疫苗后，预计美国2021年4月COVID-19累计确诊人数达31 864 970人，若未接种疫苗，累积确诊人数达35 317 082人，相差345余万人。英国按计划接种疫苗后预计4月初累积确诊人数达4 195 538人，若未接种疫苗情况下累积确诊人数达4 268 786人，相差7万余人。结论 SVEPIUHDR模型对美、英两国COVID-19疫情的预测效果较好。 |
| English Abstract: |
| Objective To fit and predict the trend of COVID-19 epidemics in the United States (USA) and the United Kingdom (UK), and analyze the effect of vaccination. Methods Based on the SEIR dynamic model, considering the presymptomatic infections, isolation measures, vaccine vaccination coverage, etc., we developed a SEIR with vaccine inoculation, Presymptomatic infectious, unconfirmed infectious, hospital isolation and domiciliary isolation dynamics model. The publicly released incidence data of COVID-19 from November 6, 2020 to January 31, 2021 in USA and from November 23, 2020 to January 31, 2021 in UK were used to fit the model and the publicly released incidence data of COVID-19 from February 1, 2021 to April 1 were used to evaluate the predicting power of the model by software R 4.0.3 and predict changes in the daily new cases in the context of different vaccination coverage.Results According to the cumulative confirmed cases, the fitting bias and the predicting bias of the SVEPIUHDR model for USA and UK were less than 5%, respectively. From the model prediction results, the cumulative cases after COVID-19 vaccination in USA in early April reached 31 864 970. If there had not had such vaccination, the cumulative cases of COVID-19 would have reached to 35 317 082, with a gap of more than 3.4 million cases. In UK, the cumulative cases of COVID-19 after the vaccination was estimated to be 4 195 538 in early April, compared with 4 268 786 cases if no COVID-19 vaccination had been provided, there would have heen a gap of more than 70 000 cases.Conclusion SVEPIUHDR model shows a good prediction effect on the epidemic of COVID-19 in both USA and UK. |
|
View Fulltext
Html FullText
View/Add Comment Download reader |
| Close |
|
|
|