National and Regional Influenza-Like-Illness Forecasts for the USA

There is demand from national health planners for forecasts of key metrics associated with influenza-like-illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016/17 season and a subsequent evaluation of our performance. We implemented a meta-population model framework with 32 specific model variants. Model variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Outputs from model variants were chosen subjectively as the the basis for our weekly forecasts. Coupled models were only available part way thought the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated early incidence when coupled models were not available. However, our forecast accuracy improved substantially once coupled solutions were available. We were able to forecast onset and peak timing and peak intensity with reasonable accuracy but without a long lead time. When we conducted retrospective forecasts for six previous seasons for which data were available, we found that the 2016/17 season was not typical: on average, coupled models performed better when fit without historically augmented data. We were able to substantially improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although the reduction of subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term. Author summary Influenza typically infects approximately 4 million people each year, resulting in 400,000 or more deaths. Influenza-like-Illness (ILI) is a practical way for health-care workers to easily estimate likely influenza cases. The Centers for Disease Control (CDC) collects and disseminates this information, and has, for the last several years, run a forecasting challenge (the CDC Flu Challenge) for modelers to predict near-term weekly incidence, week of season onset, week of peak, and intensity of peak. We have developed a modeling framework that accounts for a range of mechanisms thought to be important for influenza transmission, such as climatic conditions, school vacations, and coupling between different regions. In this study we describe our forecast procedure for the 2016/2017 season and highlight which aspects resulted in better or worse forecasts. Most notably, we found that when the dynamics of different regions are coupled together, the forecast accuracy improves. We also found that the most accurate forecasts required some level of forecaster interaction, that is, the procedure could not be completely automated without a reduction in accuracy.