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Multiscale influenza forecasting

Author

Listed:
  • Dave Osthus

    (Statistical Sciences Group)

  • Kelly R. Moran

    (Statistical Sciences Group
    Duke University)

Abstract

Influenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure and generates coherent forecasts across state, regional, and national scales. We retrospectively compare Dante’s short-term and seasonal forecasts for previous flu seasons to the Dynamic Bayesian Model (DBM), a leading competitor. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. Dante’s sharper and more accurate forecasts also suggest greater public health utility. Dante placed 1st in the Centers for Disease Control and Prevention’s prospective 2018/19 FluSight challenge in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other seasonal disease forecasting contexts having nested geographic scales of interest.

Suggested Citation

  • Dave Osthus & Kelly R. Moran, 2021. "Multiscale influenza forecasting," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23234-5
    DOI: 10.1038/s41467-021-23234-5
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    Cited by:

    1. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    2. David A. Buch & James E. Johndrow & David B. Dunson, 2023. "Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model," Biometrics, The International Biometric Society, vol. 79(4), pages 2987-2997, December.

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