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. 2020 Nov 18;10(1):20021.
doi: 10.1038/s41598-020-77242-4.

The spatiotemporal estimation of the risk and the international transmission of COVID-19: a global perspective

Yuan-Chien Lin  1   2 , Wan-Ju Chi  3 , Yu-Ting Lin  3 , Chun-Yeh Lai  3
Affiliations

The spatiotemporal estimation of the risk and the international transmission of COVID-19: a global perspective

Yuan-Chien Lin et al. Sci Rep. .

Abstract

An ongoing novel coronavirus outbreak (COVID-19) started in Wuhan, China, in December 2019. Currently, the spatiotemporal epidemic transmission, prediction, and risk are insufficient for COVID-19 but we urgently need relevant information globally. We have developed a novel two-stage simulation model to simulate the spatiotemporal changes in the number of cases and estimate the future worldwide risk. Simulation results show that if there is no specific medicine for it, it will form a global pandemic. Taiwan, South Korea, Hong Kong, Japan, Thailand, and the United States are the most vulnerable. The relationship between each country's vulnerability and days before the first imported case occurred shows an exponential decrease. We successfully predicted the outbreak of South Korea, Japan, and Italy in the early stages of the global pandemic based on the information before February 12, 2020. The development of the epidemic is now earlier than we expected. However, the trend of spread is similar to our estimation.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The cumulated confirmed case numbers of all provinces in China for 2020/1/21–2020/2/12 (red: Hubei, gray: all other provinces).
Figure 2
Figure 2
Global vulnerability map for the COVID-19 outbreak from China. The map is generated by YCL in Python 3.7 (https://www.python.org/) through the development environment Anaconda 5.3.0 (https://www.anaconda.com/).
Figure 3
Figure 3
The relationship between each country’s vulnerability and how many days it took before the first imported case occurred. The fitted regression function is y = 10.93exp(−3.32x) + 0.19.
Figure 4
Figure 4
The relationship between vulnerability and the cumulative number of cases in each country before 2020–02-12. The coefficient of the OLS Regression equation is 35.94 with R2 = 0.587 and 95% C.I = [26.896, 44.994].
Figure 5
Figure 5
Time series of the 1,000 countries simulated case numbers after the second-stage outbreak in the future using Monte Carlo simulation.
Figure 6
Figure 6
The simulated number of potential cases in four different scenarios: high, medium, low, and the extreme scenarios of Hubei Province.
Figure 7
Figure 7
The simulated cumulative case maps for conservative scenarios of A1, B1, C1, and D1 in different time-slices. The maps is generated by YCL in Python 3.7 (https://www.python.org/) through the development environment Anaconda 5.3.0 (https://www.anaconda.com/).
Figure 8
Figure 8
The simulated cumulative case maps for severe scenarios of A2, B2, C2, and D2 in different time-slices. The maps is generated by YCL in Python 3.7 (https://www.python.org/) through the development environment Anaconda 5.3.0 (https://www.anaconda.com/).
Figure 9
Figure 9
A dynamic risk map for scenario C2 at different time slices (a) 2020/2/29, (b) 2020/3/31, and (c) 2020/4/30. The maps is generated by YCL in Python 3.7 (https://www.python.org/) through the development environment Anaconda 5.3.0 (https://www.anaconda.com/).
See this image and copyright information in PMC

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