Complementing the Power of Deep Learning with Statistical Model Fusion: Probabilistic Forecasting of Influenza in Dallas County, Texas, USA

dc.contributor.VIAF315611866 (Gel, YR)
dc.contributor.authorSoliman, Marwah
dc.contributor.authorLyubchich, V.
dc.contributor.authorGel, Yulia R.
dc.contributor.utdAuthorSoliman, Marwah
dc.contributor.utdAuthorGel, Yulia R.
dc.date.accessioned2020-07-17T16:44:26Z
dc.date.available2020-07-17T16:44:26Z
dc.date.issued2019-06-08
dc.description.abstractInfluenza is one of the main causes of death, not only in the USA but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for forecasting of any upcoming influenza outbreaks. Most currently available methods for influenza prediction are based on parametric time series and regression models that impose restrictive and often unverifiable assumptions on the data. In turn, more flexible machine learning models and, particularly, deep learning tools whose utility is proven in a wide range of disciplines, remain largely under-explored in epidemiological forecasting. We study the seasonal influenza in Dallas County by evaluating the forecasting ability of deep learning with feedforward neural networks as well as performance of more conventional statistical models, such as beta regression, autoregressive integrated moving average (ARIMA), least absolute shrinkage and selection operators (LASSO), and non-parametric multivariate adaptive regression splines (MARS) models for one week and two weeks ahead forecasting. Furthermore, we assess forecasting utility of Google search queries and meteorological data as exogenous predictors of influenza activity. Finally, we develop a probabilistic forecasting of influenza in Dallas County by fusing all the considered models using Bayesian model averaging. ©2019 The Authors
dc.description.departmentSchool of Natural Sciences and Mathematics
dc.identifier.bibliographicCitationSoliman, M., V. Lyubchich, and Y. R. Gel. 2019. "Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA." Epidemics 28: art. 100345, doi: 10.1016/j.epidem.2019.05.004
dc.identifier.issn1755-4365
dc.identifier.urihttp://dx.doi.org/10.1016/j.epidem.2019.05.004
dc.identifier.urihttps://hdl.handle.net/10735.1/8715
dc.identifier.volume28
dc.language.isoen
dc.publisherElsevier B.V.
dc.rightsCC BY 4.0 (Attribution)
dc.rights©2019 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.journalEpidemics
dc.subjectBox-Jenkins forecasting
dc.subjectRegression analysis
dc.subjectEpidemics
dc.subjectInfluenza
dc.subjectDallas County (Tex.)
dc.titleComplementing the Power of Deep Learning with Statistical Model Fusion: Probabilistic Forecasting of Influenza in Dallas County, Texas, USA
dc.type.genrearticle

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