Accurate influenza forecasts using type-specific incidence data for small geographic units

Author summary Forecasting influenza is a public health priority because it allows better planning by both policy makers and healthcare facilities. Most seasonal influenza forecasting is currently undertaken for large geographical areas and is based on the number of people who have the signs and symptoms of influenza. Different types of models can provide roughly equivalent forecasts for these data: models that use the opinion of experts, statistical models, and models that reflect the underlying biology. Newly developed data streams are resolving ever smaller spatial scales, but the impacts of coupling and smaller resolution on type-specific forecasting have not been well established. In addition to the use of local point-of-care type-specific influenza data, we assess the effects of county-to-county coupling and specific humidity on forecast outcomes. Here, we show that if the data are available for smaller geographical areas and are from actual tests for influenza, they can be forecasted accurately by models that represent the underlying biological mechanisms. Further, we add to growing evidence that mechanistic models perform better on type-specific data than they do on symptom-based data, which can be triggered by a number of respiratory viruses and/or influenza types. Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.