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This study introduces a Bayesian Hidden Markov Model (HMM) using continuous test results for improved infectious disease surveillance. The new model enhances accuracy in tracking diseases like bovine viral diarrhoea virus (BVDV) in pooled samples.

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Area of Science:

  • Veterinary epidemiology
  • Statistical modeling
  • Infectious disease surveillance

Background:

  • Traditional infectious disease surveillance often uses dichotomized test results, losing valuable information.
  • Pooled sampling, like bulk tank milk (BTM), presents challenges for accurate disease monitoring.
  • Hidden Markov Models (HMMs) offer a framework for analyzing temporal disease dynamics but can be limited by data discretization.

Purpose of the Study:

  • To develop and validate a Bayesian Hidden Markov Model (HMM) that integrates continuous test results for enhanced infectious disease surveillance.
  • To improve parameter estimation and diagnostic accuracy compared to models using dichotomized data.
  • To apply the model to real-world data for evaluating diagnostic tests and disease dynamics in cattle.

Main Methods:

  • Developed a Bayesian HMM incorporating continuous test data modeled as mixtures of normal distributions.
  • Conducted simulations to compare the continuous HMM against a discrete HMM under various epidemiological scenarios.
  • Applied the model to longitudinal bovine viral diarrhoea virus (BVDV) surveillance data from Brittany, France (2014-2020).

Main Results:

  • The continuous HMM consistently outperformed the discrete version, particularly with higher infection incidence and frequent state changes.
  • The model accurately estimated dynamic parameters, diagnostic sensitivity, and specificity.
  • Analysis of BVDV data revealed stable test characteristics, confirmed ELISA test sensitivities, and indicated low transition rates to seropositivity with high persistence.

Conclusions:

  • The continuous HMM provides a robust framework for disease surveillance, diagnostic test evaluation, and threshold selection using longitudinal data.
  • The model demonstrated its adaptability for diverse epidemiological contexts, including cattle disease monitoring.
  • Future work can enhance the model by incorporating covariates and addressing potential misclassification issues.