| 唐三一,肖燕妮,彭志行,沈洪兵.新型冠状病毒肺炎疫情预测建模、数据融合与防控策略分析[J].Chinese journal of Epidemiology,2020,41(4):480-484 |
| 新型冠状病毒肺炎疫情预测建模、数据融合与防控策略分析 |
| Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak |
| Received:February 16, 2020 |
| DOI：10.3760/cma.j.cn112338-20200216-00107 |
| KeyWord: 新型冠状病毒肺炎 动力学模型 基本再生数 |
| English Key Word: COVID-19 Dynamical model Reproductive number |
| FundProject:国家自然科学基金（81673275）；国家科技重大专项（2018ZX10715002-004-002，2018ZX10713001-001） |
| Author Name | Affiliation | E-mail | | Tang Sanyi | School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China | | | Xiao Yanni | Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China | | | Peng Zhihang | Center for Global Health, Nanjing Medical University, Nanjing 210029, China | zhihangpeng@njmu.edu.cn | | Shen Hongbing | School of Public Health, Nanjing Medical University, Nanjing 211166, China | hbshen@njmu.edu.cn |
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| Abstract: |
| 自2019年12月以来,武汉暴发的COVID-19疫情由于春节人口流动快速蔓延,自2020年1月23日起全国大范围实施围堵缓疫策略,并不断提高检测和检出率,有效地抑制了疫情快速蔓延的趋势。在COVID-19爆发的早期,如何利用数学模型并结合少量和实时更新的多源数据,对疫情进行风险分析,评估防控策略的有效性和时效性等具有非常重要的现实意义。本研究将结合前期研究基础,系统介绍如何依据疫情发展的不同阶段和数据的完善,逐步建立符合我国防控策略的COVID-19传播动力学模型,给出模型由自治到非自治,风险评估指标由基本再生数到有效再生数,疫情发展与评估由早期的SEIHR传播动力学决定到最终取决于隔离人群和疑似人群规模的演变等的重要研究思路。 |
| English Abstract: |
| Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes. |
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