Geodesic
Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach
Junyuan Yang1, 2 ; Guoqiang Wang1, 2 ; Shuo Zhang1, 2 ; Fei Xu3 ; Xuezhi Li4
1 Complex Systems Research Center, Shanxi University, ShanXi TaiYuan 030006, PR China.
2 Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, PR China.
3 Department of Mathematics, Wilfrid Laurier University, University Avenue, Waterloo N2L 3C5, Canada.
4 School of Mathematics and Science, Henan Normal University, Xinxiang 453000, PR China.
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 39.

Voir la notice de l'article provenant de la source EDP Sciences

The novel coronavirus (SARS-Cov-2) has raged in mainland China for nearly three months resulting in a huge threat to people's health and economic development. According to the cumulative numbers of confirmed cases and deathes of SARS-COV-2 infection announced by the National Health Commission of China, we divided the human population into four subgroups including the adolescents group (0–19 yr old), the youth group (20–49 yr old), the middle-aged group (50–74 yr old) and the elderly group (over 75 yr old), and proposed a discrete age-structured SEIHRQ SARS-COV-2 transmission model. We utilized contact matrixes to describe the contact heterogeneities and correlations among different age groups. Adopting the Markov chain Monte Carlo (MCMC) algorithm, we identified the parameters of the model and fitted the confirmed cases from January 24th to March 31st. Through a more in-depth study, we showed that before January 28th (95% CI [Feb. 25th, Feb. 31st]), the effective reproduction number was greater than 1 and after that day its value was less than 1. Moreover, we estimated that the peak values of infection were 66 (95% CI [65,67]) for the adolescents, 3996 (95% CI [3957,4036]) for the young group, 14714 (95% CI [14692,14735]) for middle-aged group and 297 (95% CI [295,300]) for elderly people, respectively; the proportions of the final sizes of SARS-COV-2 infection accounted for less than 90% for each group. We found that under the current restricted control strategies, the most severe and high-risk group was middle-aged people aged between 50–74 yr old; without any prevention, the most severe and high-risk group had become the young adults aged 20–49 yr old.
DOI : 10.1051/mmnp/2020032

Junyuan Yang 1, 2 ; Guoqiang Wang 1, 2 ; Shuo Zhang 1, 2 ; Fei Xu 3 ; Xuezhi Li 4

1 Complex Systems Research Center, Shanxi University, ShanXi TaiYuan 030006, PR China.
2 Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, PR China.
3 Department of Mathematics, Wilfrid Laurier University, University Avenue, Waterloo N2L 3C5, Canada.
4 School of Mathematics and Science, Henan Normal University, Xinxiang 453000, PR China. 
@article{MMNP_2020_15_a13,
     author = {Junyuan Yang and Guoqiang Wang and Shuo Zhang and Fei Xu and Xuezhi Li},
     title = {Analysis of the age-structured epidemiological characteristics of {SARS-COV-2} transmission in mainland {China:} {An} aggregated approach},
     journal = {Mathematical modelling of natural phenomena},
     eid = {39},
     publisher = {mathdoc},
     volume = {15},
     year = {2020},
     doi = {10.1051/mmnp/2020032},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020032/}
}
TY  - JOUR
AU  - Junyuan Yang
AU  - Guoqiang Wang
AU  - Shuo Zhang
AU  - Fei Xu
AU  - Xuezhi Li
TI  - Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach
JO  - Mathematical modelling of natural phenomena
PY  - 2020
VL  - 15
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020032/
DO  - 10.1051/mmnp/2020032
LA  - en
ID  - MMNP_2020_15_a13
ER  - 
%0 Journal Article
%A Junyuan Yang
%A Guoqiang Wang
%A Shuo Zhang
%A Fei Xu
%A Xuezhi Li
%T Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach
%J Mathematical modelling of natural phenomena
%D 2020
%V 15
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020032/
%R 10.1051/mmnp/2020032
%G en
%F MMNP_2020_15_a13
Junyuan Yang; Guoqiang Wang; Shuo Zhang; Fei Xu; Xuezhi Li. Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 39. doi : 10.1051/mmnp/2020032. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020032/

[1] C.T. Bauch, J.O. Lloyd-Smith, M.P. Coffee, Ap Galvani Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future Epidemiology 2005 791 801

[2] D. Gamerman and H.F. Lopes, Markov chain Monte Carlo: stochastic simulation for Bayesian inference. Chapman and Hall/, Boca Raton (2006).

[3] A.J. Kucharski, T.W. Russell, C. Diamond Early dynamics of transmission and control of COVID-19: a mathematical modelling study Lancet Infect. Dis 2020

[4] X.Z. Li, J.Y. Yang and M. Martcheva, Age structured epidemic modelling, Interdisciplinary Applied Mathematics. Cham, Switzerland (2020) 11, 6330

[5] Q. Li, X.H. Guan, P. Wu Early transmision dynamics in Wuhan, China, of noval coronavirus-infected pneumonia New England J. Med. 2020 1199 1207

[6] Z. Liu, P. Magal, O. Seydi and G. Webb, Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data. Preprint medRxiv (2020). https://arxiv.org/abs/2002.12298.

[7] X. Luo, S. Feng, J. Yang Analysis of potential risk of COVID-19 infections in China based on a pairwise epidemic model 2020

[8] M.I. Meltzer, M. Gambhir, C.Y. Atkins, D.L. Swerdlow Standardizing scenarios to assess the need to respond to an influenza pandemic Clin. Infect. Dis 2015 S1 8

[9] K. Prem, A.R. Cook, M. Jim Projecting social contact matrices in 152 countries using contact surveys and demographic data Plos. Comput. Biol 2017 e1005697

[10] J.M. Read, J. Lessler, S. Riley, S. Wang, L.J. Tan, K.O. Kwok, Y. Guan, C.Q. Jiang, D.A.T. Cummings Social mixing patterns in rural and urban areas of southern China Proc. R. Soc. B 2014 20140268

[11] P. Shao and Y.G. Shan, Beware of asymptomatic transmission: study on 2019-nCov preventtion and control measures based on SEIR model (2020). https://doi.org/10.1101/2020.01.28.923169.

[12] The epidemiological characteristics of an outbreakof 2019 Novel coronavirus disease (COVID-19)-China, 2020 CCDC Weekly 2020 113 122

[13] P. Van Driessche, J. Watmough Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission Math. Biosci 2002 29 48

[14] J. Wallinga, P. Teunis, M. Kretzschmar. Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents Am. J. Epidemiol 2006 936 944

[15] S.L. Zhao, H. Chen Modelling the epidemic dynamics and control of COVID-19 outbreak in China Quant. Biol 2020

[16] Chinese Control Disease Center and Prevention. The trends and risk estimation of COVID-19. (2020). http://www.chinacdc.cn/jkzt/crb/zl/szkb11803/jszl11811/202001/P020200127544648420736.pdf.

[17] Official news on the change of diagnosed approaches issused by the Chinese govenment (in Chinese) 2020

[18] Chinese Population Statistic Yearbook 2019. http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm.

[19] 2019 novel coronavirus (in Chinese). https://baike.baidu.com/item/2019novelcoronavirus/24267858?fr=aladdin.

[20] The National Health Commission of the People's Republic of China. http://www.nhc.gov.cn/xcs/yqtb/listgzbd.shtml.

[21] China News. Mayor of Wuhan: More than 5 million people left Wuhan. 2020. http://ent.chinanews.com/sh/2020/01-26/9070484.shtml.

[22] Chinese Control Disease Center and Prevention. http://www.chinacdc.cn/jkzt/crb/zl/szkb11803/jszl11809.

Cité par Sources :