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

Bo Yang; Yunyuan Yang; Wei Zheng; Yanmei Li; Xinping Yang

doi:doi:10.11648/j.ijsd.20230901.15

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

Bo Yang, Yunyuan Yang, Wei Zheng, Yanmei Li, Xinping Yang

Published in International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)

Received: 24 February 2023 Accepted: 20 March 2023 Published: 31 March 2023

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Abstract

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%).

Published in	International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)
DOI	10.11648/j.ijsd.20230901.15
Page(s)	41-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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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. https://doi.org/10.11648/j.ijsd.20230901.15

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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

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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

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@article{10.11648/j.ijsd.20230901.15,
  author = {Bo Yang and Yunyuan Yang and Wei Zheng and Yanmei Li and Xinping Yang},
  title = {Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {41-48},
  doi = {10.11648/j.ijsd.20230901.15},
  url = {https://doi.org/10.11648/j.ijsd.20230901.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.15},
  abstract = {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%).},
 year = {2023}
}

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TY  - JOUR
T1  - Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China
AU  - Bo Yang
AU  - Yunyuan Yang
AU  - Wei Zheng
AU  - Yanmei Li
AU  - Xinping Yang
Y1  - 2023/03/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.15
DO  - 10.11648/j.ijsd.20230901.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 41
EP  - 48
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.15
AB  - 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%).
VL  - 9
IS  - 1
ER  -

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Author Information

Bo Yang

School of Mathematics and Computer Science, Chuxiong Normal University, Chuxiong, China
Yunyuan Yang

School of Environment and Chemistry, Chuxiong Normal University, Chuxiong, China
Wei Zheng

School of Mathematics and Computer Science, Chuxiong Normal University, Chuxiong, China
Yanmei Li

School of Mathematics and Computer Science, Chuxiong Normal University, Chuxiong, China
Xinping Yang

School of Mathematics and Computer Science, Chuxiong Normal University, Chuxiong, China

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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. https://doi.org/10.11648/j.ijsd.20230901.15

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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

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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

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@article{10.11648/j.ijsd.20230901.15,
  author = {Bo Yang and Yunyuan Yang and Wei Zheng and Yanmei Li and Xinping Yang},
  title = {Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {41-48},
  doi = {10.11648/j.ijsd.20230901.15},
  url = {https://doi.org/10.11648/j.ijsd.20230901.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.15},
  abstract = {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%).},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Analysis on the Spatial Difference of Input Risk of Overseas Cases of COVID-19 in China
AU  - Bo Yang
AU  - Yunyuan Yang
AU  - Wei Zheng
AU  - Yanmei Li
AU  - Xinping Yang
Y1  - 2023/03/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.15
DO  - 10.11648/j.ijsd.20230901.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 41
EP  - 48
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.15
AB  - 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%).
VL  - 9
IS  - 1
ER  -

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