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. 2020 Feb 7;9(2):462.
doi: 10.3390/jcm9020462.

Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

Biao Tang  1   2 , Xia Wang  3 , Qian Li  2   4 , Nicola Luigi Bragazzi  2 , Sanyi Tang  3 , Yanni Xiao  1   4 , Jianhong Wu  1   2   5
Affiliations

Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

Biao Tang et al. J Clin Med. .

Abstract

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling 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. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses show 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 adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. 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 they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

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

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(A) Cumulative diagnoses and revised case data (dataRev1) in mainland China, the blue curve is the best fitting curve of model (1) to dataRev1. (B) Data information of cumulative quarantined/released population.
Figure 2
Figure 2
Diagram of the model adopted in the study for simulating the novel coronavirus (2019-nCoV) infection. Interventions including intensive contact tracing followed by quarantine and isolation are indicated.
Figure 3
Figure 3
Sensitivity analyses with respect to contact rate, c (A,B), and quarantine rate, q (C,D), on the log number of infected individuals and cumulative reported cases.
Figure 4
Figure 4
Contour plot of R_c, with the parameter of baseline transmission probability and the contact rate, c (A), or the quarantine rate, q (B). (B) shows that a higher transmission probability of the virus will significantly increase the basic reproduction number.
Figure 5
Figure 5
Heat-map showing the spreading of the Coronavirus infection.
Figure 6
Figure 6
The effects of no travel restrictions (A) versus travel restriction (B) in the Hubei Province on the Coronavirus disease in Beijing city.
See this image and copyright information in PMC

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