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Age‐related model for estimating the symptomatic and asymptomatic transmissibility of COVID‐19 patients

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  • Jianbin Tan
  • Ye Shen
  • Yang Ge
  • Leonardo Martinez
  • Hui Huang

Abstract

Estimation of age‐dependent transmissibility of COVID‐19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID‐19 cases, we propose an ordinary differential equation model that considers age‐dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi‐source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age‐structured data. The added values of accounting for age‐dependent transmissibility are further evaluated through simulation studies. In real‐data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Suggested Citation

  • Jianbin Tan & Ye Shen & Yang Ge & Leonardo Martinez & Hui Huang, 2023. "Age‐related model for estimating the symptomatic and asymptomatic transmissibility of COVID‐19 patients," Biometrics, The International Biometric Society, vol. 79(3), pages 2525-2536, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2525-2536
    DOI: 10.1111/biom.13814
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    References listed on IDEAS

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    1. Frasso, Gianluca & Lambert, Philippe, 2016. "Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone," LIDAM Reprints ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Phenyo E. Lekone & Bärbel F. Finkenstädt, 2006. "Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study," Biometrics, The International Biometric Society, vol. 62(4), pages 1170-1177, December.
    3. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    4. Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Ting Tian & Jianbin Tan & Wenxiang Luo & Yukang Jiang & Minqiong Chen & Songpan Yang & Canhong Wen & Wenliang Pan & Xueqin Wang, 2021. "The Effects of Stringent and Mild Interventions for Coronavirus Pandemic," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 481-491, April.
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