Individual-Level Modelling of Infectious Disease Data: EpiILM
| Autor: | K V Vineetha Warriyar, Waleed Almutiry, Rob Deardon |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Fortran Computer science 030231 tropical medicine Inference Contact network computer.software_genre Statistics - Applications 03 medical and health sciences symbols.namesake 0302 clinical medicine Applications (stat.AP) 030212 general & internal medicine Quantitative Biology - Populations and Evolution computer.programming_language Numerical Analysis Infectious disease transmission Populations and Evolution (q-bio.PE) Markov chain Monte Carlo Individual level 3. Good health R package Infectious disease (medical specialty) FOS: Biological sciences symbols Data mining Statistics Probability and Uncertainty G.3.Statistical Software computer |
| DOI: | 10.48550/arxiv.2003.04963 |
| Popis: | In this article, we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis-Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. The use of the package is demonstrated through examples involving both simulated and real data. Comment: 15 pages, 10 figures. This paper will be submitted to the R journal |
| Databáze: | OpenAIRE |
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