TY - JOUR
T1 - TSIR
T2 - An R package for time-series susceptible-infected-recovered models of epidemics
AU - Becker, Alexander D.
AU - Grenfell, Bryan T.
N1 - Funding Information:
A.D.B. was supported by a National Science Foundation Graduate Research Fellowship and the Center for Health and Wellbeing at Princeton University. B.T.G. was supported by the Research and Policy for Infectious Disease Dynamics program of the Science and Technology Directorate, Department of Homeland Security, the Fogarty International Center, National Institutes of Health, the Bill and Melinda Gates Foundation, and the U.S. Centers for Disease Control and Prevention. We thank Ottar Bjørnstad, Quentin Caudron, Benjamin Dalziel, Ayesha S. Mahmud, C. Jessica Metcalf, Sinead E. Morris, Saki Takahashi, Amy Wesolowski, and Colin Worby for their valuable suggestions, comments, and work using the TSIR model.
Publisher Copyright:
© 2017 Becker, Grenfell. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0185528
DO - 10.1371/journal.pone.0185528
M3 - Article
C2 - 28957408
AN - SCOPUS:85031666707
SN - 1932-6203
VL - 12
JO - PLoS One
JF - PLoS One
IS - 9
M1 - e0185528
ER -