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COMPSTAT pp 41-52 | Cite as

Spatio-temporal hierarchical modeling of an infectious disease from (simulated) count data

  • Noel Cressie
  • Andrew S. Mugglin

Abstract

An infectious disease spreads through “contact” between an individual who has the disease and one who does not. However, modeling the individual-level mechanism directly requires data that would amount to observing (imperfectly) all individuals’ disease statuses along their space-time lines in the region and time period of interest. More likely, data consist of spatio-temporal aggregations that give small-area counts of the number infected during successive, regular time intervals. In this paper, we give a spatially descriptive, temporally dynamic hierarchical model to be fitted to such data. The dynamics of infection are described by just a few parameters, which can be interpreted. We take a Bayesian approach to the analysis of these space-time count data, using Markov chain Monte Carlo to compute Bayes estimates of all parameters of interest. As a “proof of concept,” we simulate data from the model and investigate how well our approach recovers important hidden features.

Keywords

small-area counts multivariate autoregression Markov chain Monte Carlo Markov random field 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Noel Cressie
    • 1
  • Andrew S. Mugglin
    • 1
  1. 1.Department of StatisticsThe Ohio State UniversityColumbusUSA

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