Geodesic
Inferring key epidemiological parameters and transmission dynamics of COVID-19 based on a modified SEIR model
Xiaoyan Wang1 ; Tianjiao Tang1 ; Lang Cao2 ; Kazuyuki Aihara3 ; Qian Guo1
1 Department of Mathematics, Shanghai Normal University, Shanghai 200234, P.R. China.
2 School of Information Engineering, Zhengzhou University, Zhengzhou 450052, P.R. China.
3 International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 74.

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This study aims to establish a model-based framework for inferring key transmission characteristics of the newly emerging outbreak of the coronavirus disease 2019 (COVID-19), especially the epidemic dynamics under quarantine conditions. Inspired by the shifting therapeutic levels and capacity at different stages of the COVID-19 pandemic, we propose a modified SEIR model with a two-phase removal rate of quarantined hosts undergoing continuously tunable transition. We employ the Markov Chain Monte Carlo (MCMC) approach for inferring and forecasting the epidemiological dynamics from the publicly available surveillance reports. The effectiveness of a short-term prediction is illustrated by adopting the data sets from 10 demographic regions including Chinese mainland and South Korea. In the surveillance period, the average ranges from 1.74 to 3.28, and the median of the mean latent period does not exceed 10 days across the surveillance regions.
DOI : 10.1051/mmnp/2020050

Xiaoyan Wang 1 ; Tianjiao Tang 1 ; Lang Cao 2 ; Kazuyuki Aihara 3 ; Qian Guo 1

1 Department of Mathematics, Shanghai Normal University, Shanghai 200234, P.R. China.
2 School of Information Engineering, Zhengzhou University, Zhengzhou 450052, P.R. China.
3 International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. 
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     title = {Inferring key epidemiological parameters and transmission dynamics of {COVID-19} based on a modified {SEIR} model},
     journal = {Mathematical modelling of natural phenomena},
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Xiaoyan Wang; Tianjiao Tang; Lang Cao; Kazuyuki Aihara; Qian Guo. Inferring key epidemiological parameters and transmission dynamics of COVID-19 based on a modified SEIR model. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 74. doi : 10.1051/mmnp/2020050. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020050/

[1] Epidemiology Working Group for NCIP Epidemic Response, The epidemiological characteristics of an outbreakof 2019 novel coronavirus diseases (COVID-19) in China Chin. J. Epidemiol 2020 145 151

[2] H. Haario, M. Laine, A. Mira, E. Saksman DRAM: efficient adaptive MCMC Stat. Comput 2006 339 354

[3] R. Li, S. Pei, B. Chen, Y. Song, T. Zhang, W. Yang, J. Shaman Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) Science 2020 489 493

[4] Q. Lin, S. Zhao, D. Gao, Y. Lou, S. Yang, S.S. Musa A conceptual model for the coronavirusdisease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action Int. J. Infect. Dis 2020 211 216

[5] M. Lipsitch Transmission dynamics and control of severe acute respiratory syndrome Science 2003 1966 1970

[6] Y. Liu, A.A. Gayle, A. Wilder-Smith, J. Rocklöv The reproductive number of COVID-19 is higher compared to SARS coronavirus J. Travel Med 2020

[7] K. Mizumoto, K. Kagaya, A. Zarebski, G. Chowell Estimating the asymptomatic proportion of coronavirusdisease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020 Eurosurveillance 2020 2000180

[8] K. Prem, Y. Liu, T.W. Russell, A.J. Kucharski, R.M. Eggo, N. Davies The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study Lancet Public Health 2020 E261 E270

[9] B. Tang, N.L. Bragazzi, Q. Li, S. Tang, Y. Xiao, J. Wu An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov) Infect. Dis. Modell 2020 248 255

[10] B. Tang, X. Wang, Q. Li, N.L. Bragazzi, S. Tang, Y. Xiao, J. Wu Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions J. Clin. Med 2020 462

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

[12] P.G. Walker, C. Whittaker, O. Watson, M. Baguelin, K.E.C. Ainslie, S. Bhatia et al., Report 12: The global impact of COVID-19 and strategies for mitigation and suppression. Technical report, Imperial College London (2020). https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gidafellowships/Imperial-College-COVID19-Global-Impact-26-03-2020v2.pdf.

[13] K. Wan, J. Chen, C. Lu, L. Dong, Z. Wu, L. Zhang When will the battle against novel coronavirus end in Wuhan: A SEIR modeling analysis J. Glob. Health 2020 011002

[14] Y. Wei, Z. Lu, Z. Du, Z. Zhang, Y. Zhao, S. Shen Fitting and forecasting the trend of COVID-19 by seir (+ caq) dynamic model Chin. J. Epidemiol 2020 470 475

[15] J.T. Wu, K. Leung, G.M. Leung Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study Lancet 2020 689 697

[16] S. Zhao, Q. Lin, J. Ran, S.S. Musa, G. Yang, W. Wang Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China,from 2019 to 2020: A data-driven analysis in the early phase of the outbreak Int. J. Infect. Dis 2020 214 217

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