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Inference in disease transmission experiments by using stochastic epidemic models
Author(s) -
Höhle Michael,
Jørgensen Erik,
O'Neill Philip D.
Publication year - 2005
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2005.00488.x
Subject(s) - observability , inference , markov chain monte carlo , computer science , disease transmission , transmission (telecommunications) , infectious disease (medical specialty) , econometrics , markov chain , disease , monte carlo method , set (abstract data type) , machine learning , statistics , artificial intelligence , bayesian probability , mathematics , biology , virology , medicine , telecommunications , pathology , programming language
Summary. The paper extends the susceptible–exposed–infective–removed model to handle heterogeneity introduced by spatially arranged populations, biologically plausible distributional assumptions and incorporation of observations from additional diagnostic tests. These extensions are motivated by a desire to analyse disease transmission experiments in a more detailed fashion than before. Such experiments are performed by veterinarians to gain knowledge about the dynamics of an infectious disease. By fitting our spatial susceptible–exposed–infective–removed with diagnostic testing model to data for a specific disease and production environment a valuable decision support tool is obtained, e.g. when evaluating on‐farm control measures. Partial observability of the epidemic process is an inherent problem when trying to estimate model parameters from experimental data. We therefore extend existing work on Markov chain Monte Carlo estimation in partially observable epidemics to the multitype epidemic set‐up of our model. Throughout the paper, data from a Belgian classical swine fever virus transmission experiment are used as a motivating example.