EpiILM Resource1 #685197 The R package EpiILM provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR). |
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- CitationsAjouter une citationList by: CiterankMapLink[3] Inference for individual level models of infectious diseases in large populations
En citant: Rob Deardon, Stephen P. Brooks, Bryan T. Grenfell, Matthew J. Keeling, Michael J. Tildesley, Nicholas J. Savill, Darren J. Shaw, Mark E. J. Woolhouse Publication date: January 2010 Publication info: Statistica Sinica, Vol. 20, No. 1 (January 2010), pp. 239-261 Cité par: David Price 1:21 PM 15 September 2022 GMT URL: |
Extrait - Individual Level Models (ILMs), a new class of models, are being applied to infectious epidemic data to aid in the understanding of the spatio-temporal dynamics of infectious diseases. These models are highly flexible and intuitive, and can be parameterised under a Bayesian framework via Markov chain Monte Carlo (MCMC) methods. Unfortunately, this parameterisation can be difficult to implement due to intense computational requirements when calculating the full posterior for large, or even moderately large, susceptible populations, or when missing data are present. Here we detail a methodology that can be used to estimate parameters for such large, and/or incomplete, data sets. This is done in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic. |