International Journal of Statistical Distributions and Applications

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China

To analyze the spatial difference of COVID-19 import risk is helpful for scientific prevention and control. On the basis of clustering 25 provinces and cities with epidemic input in study time, a multinomial distribution model was established under the Bayesian framework. All parameters Bayesian estimation was obtained by MCMC method. 25 provinces and cities with overseas input were divided into 9 categories from March 3 to April 23, 2020. 468 overseas input risk values are regarded as parameters, and the maximum MC-error estimated by Bayesian is only 0.677% of the standard deviation. During the study period, 25 provinces and cities have input risk. The highest risk areas of overseas import are 12 provinces and cities in the first category represented by Beijing, Shanghai and Guangdong Province, including 10 provinces and cities along the coast / border. The lowest risk areas are the eighth category (Henan Province) and the ninth category (Anhui Province); the fourth category (Heilongjiang Province and Shanxi Province) risk is higher than the first category in 7 days and it has the largest input vary fluctuation. Taking 2020-3-22, 4-7 and 4-18 as time nodes, the overseas input risk is divided into four stages. In the first stages, the highest risk of overseas import is the first category (59.613%); in the second and third stages are the first category (decline from 60.505% to 37.056%), the fourth category (increase from 16.071% to 33.852%); in the fourth stage, the first category (42.622%), the third category (Shaanxi Province and Jilin Province, 17.556%) and the fourth category (10.056%).

COVID-19, Overseas Input Risk, Multiple Distribution, MCMC Method, Bayesian Estimation

APA Style

Bo Yang, Yunyuan Yang, Wei Zheng, Yanmei Li, Xinping Yang. (2023). Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China. International Journal of Statistical Distributions and Applications, 9(1), 41-48.

ACS Style

Bo Yang; Yunyuan Yang; Wei Zheng; Yanmei Li; Xinping Yang. Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China. Int. J. Stat. Distrib. Appl. 2023, 9(1), 41-48. doi: 10.11648/j.ijsd.20230901.15

AMA Style

Bo Yang, Yunyuan Yang, Wei Zheng, Yanmei Li, Xinping Yang. Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China. Int J Stat Distrib Appl. 2023;9(1):41-48. doi: 10.11648/j.ijsd.20230901.15

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Novel coronavirus pneumonia and control Special expert group of the chinese preventive medicine association. An update on the epidemiological characteristics of novel coronavirus pneumonia (COVID-19) [J]. Chinese Journal of Epidemiology, 2020, 4 (02): 139-144.
2. Epidemiology group of emergency response mechansim of the COVID-19. Chinese center for disease control and prevention. Analysis of epidemiological characteristics of the COVID-19 [J]. Chinese Journal of Epidemiology, 2020, 4 (02): 145-151.
3. Biao Tang, Xia Wang, Qian Li. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions [J]. Journal of Clinical Medicine, 2020. doi: 10.3390/jcm9020462.
4. Shengli Cao, peihua Feng, pengpeng Shi. Aplication of the modified SEIR epidemic dynamics model to prediction and evaluation of the 2019 coronavirus epidemic [J]. Journal of Zhejiang University (Medical Edition). 2020. DOI: 10.3785/j.issn.1008-9292.2020.02.05.
5. Zifeng Yang, Zhiqi Zeng, Ke Wang, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions [J]. Journal of Thoracic Disease. doi: 10.21037/jtd.2020.02.64.
6. Domenico Benvenuto, Marta Giovanetti, Lazzaro Vassallo, et al. Application of the ARIMA model on the COVID-2019 epidemic dataset [J], Data in brief, 2020. doi: 10.1016/j.dib.2020.105340.
7. Adam J Kucharski, Timothy W Russell, Charlie Diamond, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study [OL/EB]. doi: /10.1101/2020.01.31.2001990.
8. Joseph T Wu, Kathy Leung, Gabriel M Leung, Nowcasting and forecasting the potential domestic and international spread of the COVID-19 outbreak originating in Wuhan, China: a modelling study [J]. The Lancet. 2020, 395 (10225): 689-697.
9. Duanbing Chen, Wei Bai, Yan Wang, Min Wang, Wuping Yu, Tao Zhou. Quantitative evaluation of prevention and control effect of new coronavirus pneumonia [J/OL]. Journal of University of Electronic Science and Technology of China: 1-6. (31 March 2020).
10. Shravan Vasishth. Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing [J]. MethodsX, 2020. DOI: 10.1016/j.mex.2020.100850.
11. Ke Han, Wangping Jia, Wenzhe Cao, Shengshu Wang, et al. Estimation of the real-time basic reproduction number of New Coronavirus pneumonia and evaluation of the current epidemic situation in first tier cities [J/OL]. Journal of the People’s Liberation Army Medical College: 1-6 (23 April 2020).
12. Ensheng Dong, Hongru Du, Gardner Lauren. An interactive web-based dashboard to track COVID-19 in real time [J]. The Lancet. Infectious diseases, 2020. DOI: 10.1016/S1473-3099(20)30120-1.
13. Weijie Guan, Zhengyi Ni, Yu Hu, et al. Clinical characteristics of coronavirus disease 2019 in China [J]. The New England Journal of Medicine. 2020. DOI: 10.1056/NEJMoa2002032.
14. Cuifang Qi, liren Yang, Zixuan Yang, Li Shang, et al. Factors affecting the provincial transmission and development of novel Coronavirus pneumonia: Based on data from 30 provinces and cities [J/OL]. Journal of xi’an Jiaotong University (Medical Edition): 1-13 (23 April 2020).
15. Dali Yi, Gaoming Li, Huiming Leng. Cluster analysis of regional differences in the development of novel Coronavirus pneumonia [J/OL]. Journal of Chongqing Medical University: 1-6 (23 April 2020).
16. Zhang Liu, Jiale Qian, Yunyan Du, et al. Multi-level spatial distribution estimation model of the interregional migrant population using multi-source spatio-temporal big data: Take population emigrated from Wuhan during the COVID-19 epidemic as an example [J]. Journal of Geoinformation Science, 2020, 22 (02): 147-160.
17. Mingxiang Feng, Zhixiang Fang, Xiongbo Lu, et al. Estimation method of temporal and spatial spread of novel coronavirus pneumonia on, the scale of traffic analysis area: A case study of wuhan city [J/OL]. Journal of Wuhan University (Information Science Edition): 1-12 (23 April 2020).
18. Xiaoqun He. Multivariate statistical analysis (5th Edition) [M]. Beijing: China Renmin University Press, 2016, 1, 4: 52-60.
19. Yi Xue. Statistical modeling and R software (1st Edition) [M]. Beijing: Tsinghua University press, 2007, 4: 402-418.
20. Nguyen X. Borrowing strengh in hierarchical bayes: Posterior concentration of the dirichlet base measure [J]. Bernoulli, 2016, 22 (3): 1535-1571.
21. Radford M Neal. Suppressing random walks in markov chain monte carlo using ordered overrelaxation [M]. Springer Netherlands, 1998. 205-230.
22. Lunn D, Jackson Christopher, et al. A Practical Introduction to Bayesian Analysis [M]. CRC Press, 2013.
23. Shisong Mao, Yiming Cheng, et al. Probability theory and mathematical statistics (2nd Edition) [M]. Beijing: Higher Education Press, 2011: 332-338.