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Characterizing infectious disease progression through discrete states using hidden Markov models.

Kristina M Ceres1, Ynte H Schukken2, Yrjö T Gröhn1

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Detecting Johne's disease early is crucial for dairy farms. This study identified two distinct Mycobacterium avium ssp. paratuberculosis (MAP) shedding patterns in cows, aiding in early detection and control of this slow-progressing infectious disease.

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Area of Science:

  • Veterinary epidemiology
  • Infectious disease modeling
  • Dairy cattle health

Background:

  • Accurate characterization of infectious disease progression is vital for effective management and transmission prevention in livestock.
  • Slowly progressing diseases, like Johne's disease caused by Mycobacterium avium ssp. paratuberculosis (MAP), present diagnostic challenges due to long latency periods.
  • Undetected MAP infection on dairy farms can lead to significant animal shedding and production losses before clinical signs appear.

Purpose of the Study:

  • To parameterize disease progression trajectories for Mycobacterium avium ssp. paratuberculosis (MAP) in dairy cattle.
  • To identify distinct disease progression pathways, specifically differentiating between high-shedding and low-shedding states.
  • To develop a model framework for early detection and targeted control of Johne's disease.

Main Methods:

  • Utilized multi-year longitudinal fecal sampling data from three US dairy farms.
  • Applied continuous-time hidden Markov models to analyze disease progression.
  • Employed a modified Baum-Welch expectation maximization algorithm for parameter estimation and posterior decoding to identify shedding patterns.

Main Results:

  • Identified two distinct fecal shedding patterns in dairy cows associated with Mycobacterium avium ssp. paratuberculosis (MAP) infection.
  • Observed distinct disease states, including a high-shedding state and a low-shedding state without apparent clinical disease.
  • The model successfully differentiated between cows exhibiting high shedding and those without.

Conclusions:

  • The developed model framework can distinguish between different Mycobacterium avium ssp. paratuberculosis (MAP) shedding patterns in dairy cattle.
  • This approach enables prospective identification of cows likely to progress to clinical Johne's disease.
  • The model framework is applicable to characterizing the progression of other slowly progressing infectious diseases in livestock.