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Bayesian multi‐level mixed‐effects model for influenza dynamics

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  • Hanwen Huang

Abstract

Influenza A viruses (IAV) are the only influenza viruses known to cause flu pandemics. Understanding the evolution of different sub‐types of IAV on their natural hosts is important for preventing and controlling the virus. We propose a mechanism‐based Bayesian multi‐level mixed‐effects model for characterising influenza viral dynamics, described by a set of ordinary differential equations (ODE). Both strain‐specific and subject‐specific random effects are included for the ODE parameters. Our models can characterise the common features in the population while taking into account the variations among individuals. The random effects selection is conducted at strain level through re‐parameterising the covariance parameters of the corresponding random effect distribution. Our method does not need to solve ODE directly. We demonstrate that the posterior computation can proceed via a simple and efficient Markov chain Monte Carlo algorithm. The methods are illustrated using simulated data and a real data from a study relating virus load estimates from influenza infections in ducks.

Suggested Citation

  • Hanwen Huang, 2022. "Bayesian multi‐level mixed‐effects model for influenza dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1978-1995, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1978-1995
    DOI: 10.1111/rssc.12603
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    References listed on IDEAS

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    5. Liu, Baisen & Wang, Liangliang & Nie, Yunlong & Cao, Jiguo, 2019. "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 233-246.
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