Multiscale Influenza Forecasting
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Influenza forecasting in the United States (US) is complex and challenging for reasons including substantial spatial and temporal variability, nested geographic scales of forecast interest, and heterogeneous surveillance participation. Here we present a flexible influenza forecasting model called Dante, a multiscale flu forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure. Forecasts at the Health and Human Services (HHS) regional and national scales are generated as linear combinations of state forecasts with weights proportional to US Census population estimates, resulting in coherent forecasts across nested geographic scales. We retrospectively compare Dante's short-term and seasonal forecasts at the state, regional, and national scales for the 2012 through 2017 flu seasons in the US to the Dynamic Bayesian Model (DBM), a leading flu forecasting model. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. The improved performance is due to Dante making forecasts, especially short-term forecasts, more confidently and accurately than DBM, suggesting Dante's improved forecast scores will also translate to more useful forecasts for the public health sector. Dante participated in the prospective 2018/19 FluSight challenge hosted by the Centers for Disease Control and Prevention and placed 1st in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other disease forecasting contexts where nested geographic scales of interest exist.
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