Browsing by Author "Huang, Hui"

Now showing 1 - 4 of 4
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    Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients.
    (Biometrics, 2023-09) Tan, Jianbin; Shen, Ye; Ge, Yang; Martinez, Leonardo; Huang, Hui
    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.
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    Graphical Principal Component Analysis of Multivariate Functional Time Series
    (Journal of the American Statistical Association) Tan, Jianbin; Liang, Decai; Guan, Yongtao; Huang, Hui
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    Green’s matching: an efficient approach to parameter estimation in complex dynamic systems
    (Journal of the Royal Statistical Society Series B: Statistical Methodology) Tan, Jianbin; Zhang, Guoyu; Wang, Xueqin; Huang, Hui; Yao, Fang
    Abstract Parameters of differential equations are essential to characterize intrinsic behaviours of dynamic systems. Numerous methods for estimating parameters in dynamic systems are computationally and/or statistically inadequate, especially for complex systems with general-order differential operators, such as motion dynamics. This article presents Green’s matching, a computationally tractable and statistically efficient two-step method, which only needs to approximate trajectories in dynamic systems but not their derivatives due to the inverse of differential operators by Green’s function. This yields a statistically optimal guarantee for parameter estimation in general-order equations, a feature not shared by existing methods, and provides an efficient framework for broad statistical inferences in complex dynamic systems.
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    Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study.
    (Epidemiology and infection, 2022-09) Tan, Jianbin; Ge, Yang; Martinez, Leonardo; Sun, Jimin; Li, Changwei; Westbrook, Adrianna; Chen, Enfu; Pan, Jinren; Li, Yang; Cheng, Wei; Ling, Feng; Chen, Zhiping; Shen, Ye; Huang, Hui
    Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9-80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0-34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.