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Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods
Author(s) -
Morton Alexander,
Finkenstädt Bärbel F.
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.1467-9876.2005.05366.x
Subject(s) - markov chain monte carlo , measles , markov chain , monte carlo method , series (stratigraphy) , inference , computer science , incidence (geometry) , epidemic model , discrete time and continuous time , time series , infectious disease (medical specialty) , disease , mathematics , statistics , medicine , virology , artificial intelligence , biology , machine learning , pathology , population , paleontology , geometry , environmental health , vaccination
Summary. A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.