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April 27, 2026Results in Control and OptimizationOpen Access

Bayesian inference for modeling seasonal influenza transmission under control measures

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Authors

RSRania SaadehNANaseam Al-KuleabFGFathelrhman EL Guma

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Overview

Randomized trial assesses vaccination impact on influenza transmission dynamics, indicating significant findings for public health strategies.

Key Points

  • This research aims to model and forecast influenza transmission dynamics under various vaccination interventions using Bayesian inference.
  • Utilized the SVEIHR model with Bayesian inference techniques to analyze influenza transmission dynamics.
  • Conducted Markov Chain Monte Carlo (MCMC) simulations with a No-U-Turn Sampler (NUTS) from 2020 to 2022 weekly confirmed cases.
  • Performed sensitivity analysis using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC) to investigate influential parameters.
  • Effective contact rate and initial exposure level were identified as the most significant parameters influencing transmission dynamics.
  • Vaccination rate showed a mild negative correlation with the spread of influenza.
  • Credible intervals (CrI) were provided for parameter estimates, ensuring robust statistical validation.

Cite This Study

Saadeh et al. (2026) studied this question.

synapsesocial.com/papers/69eefde9fede9185760d4b8ahttps://doi.org/10.1016/j.rico.2026.100714
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