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An integer-valued time series model for multivariate surveillance.

Xanthi Pedeli1,2, Dimitris Karlis1

  • 1Department of Statistics, Athens University of Economics and Business, Athens, Greece.

Statistics in Medicine
|December 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing complex public health surveillance data. The model effectively handles correlated, count-based data to detect disease outbreaks.

Keywords:
correlationcount datainteger-valued time seriesmultivariate surveillance

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Area of Science:

  • Public Health
  • Biostatistics
  • Epidemiology

Background:

  • Increasing availability of multivariate public health surveillance data necessitates advanced statistical methods.
  • Existing methods often fail to address both the integer-valued nature and complex correlation structures (temporal and cross-correlation) of surveillance data.
  • There is a growing demand for statistical approaches that can effectively manage multivariate surveillance scenarios.

Purpose of the Study:

  • To propose a novel multivariate integer-valued autoregressive model for public health surveillance.
  • To develop a model capable of handling overdispersion, covariate information, and both serial and cross-correlations.
  • To enable natural decomposition into endemic and epidemic components for infectious disease dynamics.

Main Methods:

  • Development of a multivariate integer-valued autoregressive (MINAR) model.
  • Incorporation of methods to account for overdispersion and covariates.
  • Utilizing one-step-ahead predictions from the fitted model on historical data for outbreak detection.

Main Results:

  • The proposed MINAR model effectively accommodates serial and cross-correlations in multivariate count data.
  • The model allows for a natural separation of endemic and epidemic disease patterns.
  • Demonstrated successful application on trivariate syndromic surveillance data from the Athens 2004 Olympic Games.

Conclusions:

  • The suggested multivariate integer-valued autoregressive model provides a robust framework for analyzing complex public health surveillance data.
  • This approach enhances the ability to detect disease outbreaks by effectively modeling temporal dependencies and correlations.
  • The model's performance is validated through a real-world case study, highlighting its practical utility in public health surveillance.