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Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases.

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This summary is machine-generated.

This study introduces a new multivariate model for analyzing infectious disease counts, accounting for disease absence and interactions. The model helps compare transmission dynamics of co-circulating diseases like dengue, Zika, and chikungunya.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Modeling

Background:

  • Univariate zero-inflated models are common for infectious disease counts with excess zeros.
  • Multivariate modeling is complex, requiring consideration of spatial, temporal, and disease correlations in both count and zero-inflated data.
  • Understanding co-circulating disease dynamics is crucial, especially when diseases exhibit periods of absence.

Purpose of the Study:

  • To develop and apply a novel multivariate statistical model for comparing spatio-temporal transmission dynamics of co-circulating infectious diseases.
  • To account for disease absence, interactions, and spatial spread in disease transmission modeling.
  • To investigate factors associated with differences in disease transmission intensity.

Main Methods:

  • A Bayesian Markov chain Monte Carlo (MCMC) approach was used for inference.
  • Coupled Markov chains model disease presence/absence dynamics, incorporating interactions and spatial spread.
  • An autoregressive multinomial model analyzes co-circulating disease counts when present, allowing for factor association analysis.

Main Results:

  • The model successfully captured spatio-temporal disease dynamics, including periods of absence for certain diseases.
  • It allowed for the comparison of transmission intensities among dengue, Zika, and chikungunya.
  • Associations between environmental factors like temperature and transmission intensity were investigated.

Conclusions:

  • The proposed multivariate model provides a robust framework for analyzing complex spatio-temporal infectious disease data with excess zeros and co-circulation.
  • The methodology is effective in comparing disease transmission dynamics and identifying influencing factors.
  • The study successfully applied the model to a real-world scenario of a triple epidemic in Rio de Janeiro.