Hierarchical statistical modelling of influenza epidemic dynamics in space and time.
An infectious disease typically spreads via contact between infected and susceptible individuals. Since the small-scale movements and contacts between people are generally not recorded, available data regarding infectious disease are often aggregations in space and time, yielding small-area counts of the number infected during successive, regular time intervals. In this paper, we develop a spatially descriptive, temporally dynamic hierarchical model to be fitted to such data. Disease counts are viewed as a realization from an underlying multivariate autoregressive process, where the relative risk of infection incorporates the space-time dynamic. We take a Bayesian approach, using Markov chain Monte Carlo to compute posterior estimates of all parameters of interest. We apply the methodology to an influenza epidemic in Scotland during the years 1989-1990.
Mugglin
Andrew S
AS
Arrhythmia Management Clinical & Outcomes Research, Medtronic Inc, 7000 Central Avenue NE, MS CW300, Minneapolis MN 55432-3576, USA. andrew.mugglin@medtronic.com
Cressie
Noel
N
Gemmell
Islay
I
D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012606:Scotland / Q000453:epidemiology; D017062:Small-Area Analysis; D013029:Space-Time Clustering