Estimation of the Transmission Risk of 2019-nCov and Its Implication for Public Health Interventions (2019-nCov的传播风险估计及其对公共卫生干预的意义)

20 Pages Posted: 27 Jan 2020

See all articles by Biao Tang

Biao Tang

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences; York University - Department of Mathematics and Statistics

Xia Wang

Shaanxi Normal University - School of Mathematics and Information Science

Qian Li

Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics; York University - Laboratory for Industrial and Applied Mathematics

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Sanyi Tang

Shaanxi Normal University - School of Mathematics and Information Science

Yanni Xiao

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences; Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics

Jianhong Wu

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences; York University - Laboratory for Industrial and Applied Mathematics; York University - Laboratory for Industrial and Applied Mathematics

Date Written: January 24, 2020

Abstract

English Abstract: Background: Since the emergence of the first pneumonia cases in Wuhan, China, the novel coronavirus (2019-nCov) infection has been quickly spreading out to other provinces and neighbouring countries. Estimation of the basic reproduction number by means of mathematical modelling can be helpful for determining the potential and severity of an outbreak, and providing critical information for identifying the type of disease interventions and intensity.

Methods: A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and the intervention measures.

Findings: The estimation results based on likelihood and model analysis reveal that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses reveal that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction of Wuhan on 2019-nCov infection in Beijing being almost equivalent to increasing quarantine by 100-thousand baseline value.

Interpretation: It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCov infection, and how long should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since January 23rd 2020) with significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in 7 days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

Mandarin Abstract: 背景:自从中国武汉出现第一例肺炎病例以来,新型冠状病毒(2019-nCov)感染已迅速传播到其他省份和周边国家。通过数学模型估计基本再生数,有助于确定疫情爆发的可能性和严重性,并为确定疾病干预类型和强度提供关键信息。

方法:根据疾病的临床进展,个体的流行病学状况和干预措施,设计确定性的仓室模型。

结果:基于似然函数和模型分析的估计结果表明,控制再生数可能高达6.47(95%CI 5.71-7.23)。敏感性分析显示,密集接触追踪和隔离等干预措施可以有效减少控制再生数和传播风险,武汉封城措施对北京2019-nCov感染的影响几乎等同于增加隔离措施10万的基线值。

解释:必须评估中国当局实施的昂贵,资源密集型措施如何有助于预防和控制2019-nCov感染,以及应维持多长时间。在最严格的措施下,预计疫情将在两周内(自2020年1月23日起)达到峰值,峰值较低。与没有出行限制的情况相比,有了出行限制(即没有输入的潜伏类个体进入北京),北京的7天感染者数量将减少91.14%。

Keywords: coronavirus, infection management and control, travel restriction, mathematical model, SEIR model

JEL Classification: C02

Suggested Citation

Tang, Biao and Wang, Xia and Li, Qian and Bragazzi, Nicola Luigi and Tang, Sanyi and Xiao, Yanni and Wu, Jianhong, Estimation of the Transmission Risk of 2019-nCov and Its Implication for Public Health Interventions (2019-nCov的传播风险估计及其对公共卫生干预的意义) (January 24, 2020). Available at SSRN: https://ssrn.com/abstract=3525558 or http://dx.doi.org/10.2139/ssrn.3525558

Biao Tang

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

York University - Department of Mathematics and Statistics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Xia Wang

Shaanxi Normal University - School of Mathematics and Information Science

China

Qian Li

Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics ( email )

China

York University - Laboratory for Industrial and Applied Mathematics

Canada

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Canada

Sanyi Tang

Shaanxi Normal University - School of Mathematics and Information Science

China

Yanni Xiao

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics ( email )

China

Jianhong Wu (Contact Author)

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences ( email )

China

York University - Laboratory for Industrial and Applied Mathematics

Canada

York University - Laboratory for Industrial and Applied Mathematics

Canada

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