﻿ 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
 中华流行病学杂志  2020, Vol. 41 Issue (4): 480-484 PDF
http://dx.doi.org/10.3760/cma.j.cn112338-20200216-00107

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#### 文章信息

Tang Sanyi, Xiao Yanni, Peng Zhihang, Shen Hongbing

Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak

Chinese Journal of Epidemiology, 2020, 41(4): 480-484
http://dx.doi.org/10.3760/cma.j.cn112338-20200216-00107

### 文章历史

1. 陕西师范大学数学与信息科学学院, 西安 710119;
2. 西安交通大学数学与统计学院数学与生命科学交叉中心 710049;
3. 南京医科大学全球健康中心 210029;
4. 南京医科大学公共卫生学院 211166

Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak
Tang Sanyi1 , Xiao Yanni2 , Peng Zhihang3 , Shen Hongbing4
1. School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China;
2. Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China;
3. Center for Global Health, Nanjing Medical University, Nanjing 210029, China;
4. School of Public Health, Nanjing Medical University, Nanjing 211166, China
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.
Key words: COVID-19    Dynamical model    Reproductive number

COVID-19疫情暴发以来，湖北省卫生健康委员会和国家卫生健康委员会及时通报了疫情数据，主要包括累积报告病例、累计治愈病例、累积死亡病例、跟踪隔离人数、疑似病例人数等，同时多个省份还报告了输入病例、本地病例等详细数据。上述详尽数据为建立数据驱动的COVID-19传播模型奠定了基础，因此，研究期间依据COVID-19传播的机制以及我国采取的防控策略，给出了如下具多防控策略的一般性COVID-19疫情传播模型示意图（图 1）。依据具有密切跟踪隔离策略建立传染病动力学模型的基本思路，根据图 1可得如下数学模型：

(1)
 注：融合多源数据、构建模型、实现预测、预警和风险分析及决策评估 图 1 基于COVID-19传播机制、防控策略的模型构建示意图

(2)

R0＝6.47是一个很大的一个值，相比2003年SARS的R0＝3.6，说明了COVID-19的传播能力远大于SARS，也说明了疫情发展的紧急性和严重性。本研究的估计值比包括WHO在内的组织或团队公布的R0＝2.2要高出很多，但本研究基于动力学模型和统计方法相互验证得到了几乎相同的数值，具有一定的可信度。2月7日WHO通过分析中国约1.7万例患者数据后指出：COVID-19的传染性远高于SARS。近期基于大样本分析，有关工作相继报道了R0＝3.7和5.5，与本研究前期结果近似，说明本研究的结果具有较强的可信性和时效性。

 图 2 从2019年12月9日到2020年2月10日之间的有效再生数估计值及其相应的95%CI

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