Statistical inference and model selection for the 1861 Hagelloch measles epidemic.
A stochastic epidemic model is proposed which incorporates heterogeneity in the spread of a disease through a population. In particular, three factors are considered: the spatial location of an individual's home and the household and school class to which the individual belongs. The model is applied to an extremely informative measles data set and the model is compared with nested models, which incorporate some, but not all, of the aforementioned factors. A reversible jump Markov chain Monte Carlo algorithm is then introduced which assists in selecting the most appropriate model to fit the data.
Neal
Peter J
PJ
Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UKP. Neal-2@umist.ac.uk
Roberts
Gareth O
GO
D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D004196:Disease Outbreaks; D005191:Family Characteristics; D005260:Female; D005858:Germany / Q000453:epidemiology; D006801:Humans; D007223:Infant; D008297:Male; D008390:Markov Chains; D008457:Measles / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D008459:Measles virus / Q000254:growth & development*; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012574:Schools; D013029:Space-Time Clustering