Forecasting seasonal influenza with a state-space SIR model

Journal Article · Sat Apr 08 00:00:00 EDT 2017 · The Annals of Applied Statistics

DOI:https://doi.org/10.1214/16-AOAS1000· OSTI ID:1406200

Osthus, Dave ^[1]; Hickmann, Kyle S. ^[2]; Caragea, Petruţa C. ^[3]; Higdon, Dave ^[4]; Del Valle, Sara Y. ^[2]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Iowa State Univ., Ames, IA (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Iowa State Univ., Ames, IA (United States)
4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

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.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1406200
• Report Number(s): LA-UR-15-26537
• Journal Information: The Annals of Applied Statistics, Vol. 11, Issue 1; ISSN 1932-6157
• Publisher: Institute of Mathematical Statistics
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
state-space models
disease forecasting
SIR model
influenza
bayesian forecasting