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Influenza forecast optimization when using different surveillance data types and geographic scale.

Abstract

Advance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction.

To assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams.

In retrospective forecasts, and across data types, there were no clear optimal combinations for the 3 system inputs; however, scaling was most critical to forecast accuracy, whereas OEV and lambda were not.

Forecasts using new data streams should be tested to determine an appropriate scaling value using historical data and analyzed for forecast accuracy.

Here, we generate retrospective influenza forecasts with a dynamic, compartmental model-inference system using surveillance data for influenza-like illness (ILI), laboratory-confirmed cases, and pneumonia and influenza mortality at state and county levels. We evaluate how specification of 3 system inputs-scaling, observational error variance (OEV), and filter divergence (lambda)-affects forecast accuracy.

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