Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT
| Autor: | Rob Deardon, Waleed Almutiry, Vineetha Warriyar K |
|---|---|
| Přispěvatelé: | Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), the Natural Sciences and Engineering Research Council of Canada (NSERC), Qassim University, University of Calgary, Eyes High Post Doctoral Scholarship scheme. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer science Bayesian probability Contact network computer.software_genre Statistics - Applications Statistics - Computation 01 natural sciences Set (abstract data type) 010104 statistics & probability symbols.namesake EpiILMCT infectious disease individual level modeling spatial contact network R Applications (stat.AP) 0101 mathematics Computation (stat.CO) Experimental data Markov chain Monte Carlo Individual level Automatic summarization Infectious disease (medical specialty) symbols Data mining Statistics Probability and Uncertainty computer Software |
| Zdroj: | Journal of Statistical Software; Vol 98 (2021); 1-44 |
| ISSN: | 1548-7660 |
| Popis: | This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SINR) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus (TSWV) disease. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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