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

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  • Alexander D Becker
  • Bryan T Grenfell

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.

Suggested Citation

  • Alexander D Becker & Bryan T Grenfell, 2017. "tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-10, September.
    • Handle: RePEc:plo:pone00:0185528
    DOI: 10.1371/journal.pone.0185528
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    References listed on IDEAS

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    1. Matthew J. Ferrari & Rebecca F. Grais & Nita Bharti & Andrew J. K. Conlan & Ottar N. Bjørnstad & Lara J. Wolfson & Philippe J. Guerin & Ali Djibo & Bryan T. Grenfell, 2008. "The dynamics of measles in sub-Saharan Africa," Nature, Nature, vol. 451(7179), pages 679-684, February.
    2. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
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    1. Rachel E. Baker & Wenchang Yang & Gabriel A. Vecchi & Saki Takahashi, 2024. "Increasing intensity of enterovirus outbreaks projected with climate change," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.

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