Predicting hospital admissions with integer-valued time series

Abstract : Prediction of seasonal epidemics have beenwidely treated in the medical literature, with various methods to forecast the future cases of a given disease, when it’s about infectious diseases: compartmental methods that forecast the number of persons at each state of the epidemic (susceptible, infected, resistant) are used(1), as well as methods based on time series (ARMA,ARCH,...)(2), this last method can be successful with large sample of data, assuming their normal distribution. Here, we propose to test time series for count data when the continuous time series are not adapted to predict health activity. The aim of this work is to predict the number of hospital admissions of future weeksat local scale, using methodsfor integer-valued time series:INAR(p) and INGARCH(p,q) methods(3–5), wehavealso test the autoregressive methods for continuous data.
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Poster communications
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https://hal-emse.ccsd.cnrs.fr/emse-02301132
Contributor : Florent Breuil <>
Submitted on : Monday, September 30, 2019 - 11:28:05 AM
Last modification on : Thursday, October 17, 2019 - 12:36:11 PM

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  • HAL Id : emse-02301132, version 1

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Radia Spiga, Mireille Batton-Hubert, Marianne Sarazin. Predicting hospital admissions with integer-valued time series. Olga Valenzuela, Fernando Rojas, Héctor Pomares, Ignacio Rojas. International Conference on Time Series and Forecasting, ITISE 2019, Sep 2019, Grenade, Spain. 2, pp.897-998, Proceedings ITISE-2019. ⟨emse-02301132⟩

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