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A coupled hidden Markov model for disease interactions.

Chris Sherlock1, Tatiana Xifara, Sandra Telfer

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|November 14, 2013
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Summary
This summary is machine-generated.

Parasite interactions in field voles were studied using a hidden Markov model. Researchers found evidence of interplay between several parasite species and identified acquired immunity in two cases.

Keywords:
Adaptive Markov chain Monte Carlo samplingForward–backward algorithmGibbs samplerHidden Markov modelsZoonosis

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Area of Science:

  • Ecology
  • Parasitology
  • Mathematical Biology

Background:

  • Understanding host-parasite dynamics is crucial for wildlife health.
  • Longitudinal studies are vital for tracking disease progression and interactions.
  • Incomplete data from repeated sampling poses analytical challenges.

Purpose of the Study:

  • To investigate complex interactions between multiple parasite species within a single host population.
  • To model the influence of one parasite's presence on the transmission dynamics of others.
  • To identify potential acquired immune responses to parasitic infections.

Main Methods:

  • Utilized a discrete-time hidden Markov model (HMM) for each of six parasite species.
  • Incorporated logistic regressions to model transition probabilities dependent on covariates.
  • Included the hidden states of other diseases as covariates for each parasite's transition probabilities.
  • Employed a Gibbs sampler with adaptive Metropolis-Hastings steps for model inference.

Main Results:

  • Identified significant interactions between several pairs of parasite species.
  • Provided evidence for an acquired immune response in field voles for two specific parasites.
  • The model successfully handled incomplete longitudinal data profiles.

Conclusions:

  • Parasite species do not act in isolation; their interactions significantly influence host infection dynamics.
  • Acquired immunity plays a role in the host-parasite relationship for certain infections.
  • Hidden Markov models offer a robust framework for analyzing complex ecological and epidemiological data with missing observations.