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Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models.

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We developed a new statistical framework to identify interactions between multiple viruses using population health data. This approach helps understand virus communities and aids public health planning.

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Area of Science:

  • Epidemiology
  • Community Ecology
  • Statistical Modeling

Background:

  • Pathogen interactions within hosts are complex and influence health.
  • Existing methods struggle to identify pathogen interactions from population-level data.
  • A need exists for robust statistical tools to analyze pathogen communities in public health surveillance.

Purpose of the Study:

  • To develop and validate a statistical framework for identifying virus-virus interactions from time-series infection data.
  • To apply this framework to real-world diagnostic data for respiratory viruses.
  • To enable a community ecology perspective in infectious disease epidemiology.

Main Methods:

  • Developed a Bayesian multivariate disease mapping framework for contemporaneous, non-stationary infection time series.
  • Accounted for within- and between-year dependencies, seasonality, demographics, and infection frequencies.
  • Validated the framework with synthetic data and applied it to diagnostic data of five respiratory viruses (2005-2013).

Main Results:

  • The framework successfully identified epidemiological interactions (positive and negative covariances) between specific virus pairs.
  • Demonstrated the ability to distinguish genuine interactions from mere correlations.
  • Revealed complex interactions among adenovirus, human coronavirus, human metapneumovirus, influenza B virus, and respiratory syncytial virus.

Conclusions:

  • The developed statistical framework enables robust identification of virus-virus interactions from population-scale data.
  • This approach facilitates applying community ecology principles to infectious disease epidemiology.
  • Findings have significant implications for public health planning and preparedness strategies.