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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5619791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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