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tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics

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...

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Detalles Bibliográficos
Autores principales: Becker, Alexander D., Grenfell, Bryan T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619791/
https://www.ncbi.nlm.nih.gov/pubmed/28957408
http://dx.doi.org/10.1371/journal.pone.0185528
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author Becker, Alexander D.
Grenfell, Bryan T.
author_facet Becker, Alexander D.
Grenfell, Bryan T.
author_sort Becker, Alexander D.
collection PubMed
description 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.
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spelling pubmed-56197912017-10-17 tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics Becker, Alexander D. Grenfell, Bryan T. PLoS One Research Article 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. Public Library of Science 2017-09-28 /pmc/articles/PMC5619791/ /pubmed/28957408 http://dx.doi.org/10.1371/journal.pone.0185528 Text en © 2017 Becker, Grenfell http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Becker, Alexander D.
Grenfell, Bryan T.
tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title_full tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title_fullStr tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title_full_unstemmed tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title_short tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics
title_sort tsir: an r package for time-series susceptible-infected-recovered models of epidemics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619791/
https://www.ncbi.nlm.nih.gov/pubmed/28957408
http://dx.doi.org/10.1371/journal.pone.0185528
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