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March 2017 Forecasting seasonal influenza with a state-space SIR model
Dave Osthus, Kyle S. Hickmann, Petruţa C. Caragea, Dave Higdon, Sara Y. Del Valle
Ann. Appl. Stat. 11(1): 202-224 (March 2017). DOI: 10.1214/16-AOAS1000


Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention’s influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.


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Dave Osthus. Kyle S. Hickmann. Petruţa C. Caragea. Dave Higdon. Sara Y. Del Valle. "Forecasting seasonal influenza with a state-space SIR model." Ann. Appl. Stat. 11 (1) 202 - 224, March 2017.


Received: 1 September 2015; Revised: 1 September 2016; Published: March 2017
First available in Project Euclid: 8 April 2017

zbMATH: 1366.62236
MathSciNet: MR3634321
Digital Object Identifier: 10.1214/16-AOAS1000

Rights: Copyright © 2017 Institute of Mathematical Statistics


Vol.11 • No. 1 • March 2017
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