1. tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
- Author
-
Alexander D. Becker and Bryan T. Grenfell
- Subjects
0301 basic medicine ,Viral Diseases ,Time Factors ,Statistical methods ,Computer science ,Epidemiology ,lcsh:Medicine ,Diagnostic tools ,computer.software_genre ,Pathology and Laboratory Medicine ,01 natural sciences ,Pediatrics ,010104 statistics & probability ,Software ,Medicine and Health Sciences ,lcsh:Science ,Multidisciplinary ,Simulation and Modeling ,Software Engineering ,Infectious Disease Epidemiology ,Monte Carlo method ,Physical sciences ,Infectious Diseases ,Engineering and Technology ,Data mining ,Disease Susceptibility ,Pathogens ,Pediatric Infections ,Research Article ,Computer and Information Sciences ,Statistics (mathematics) ,Research and Analysis Methods ,Measles ,Models, Biological ,03 medical and health sciences ,medicine ,Humans ,Computer Simulation ,Disease Dynamics ,0101 mathematics ,Epidemics ,business.industry ,Software Tools ,lcsh:R ,R Programming Language ,medicine.disease ,R package ,030104 developmental biology ,Infectious disease (medical specialty) ,Mathematical and statistical techniques ,Programming Languages ,lcsh:Q ,business ,computer ,Mathematics - Abstract
tsiR is an open source software package implemented in the R programming language designed to analyze infectious disease time-series data. The software extends a well-studied and widely-applied algorithm, the time-series Susceptible-Infected-Recovered (TSIR) model, to infer parameters from incidence data, such as contact seasonality, and to forward simulate the underlying mechanistic model. The tsiR package aggregates a number of different fitting features previously described in the literature in a user-friendly way, providing support for their broader adoption in infectious disease research. Also included in tsiR are a number of diagnostic tools to assess the fit of the TSIR model. This package should be useful for researchers analyzing incidence data for fully-immunizing infectious diseases.
- Published
- 2017