A new twist has been added to the annual CDC flu forecasting challenge by University of Massachusetts Amherst researchers who believe forecasting can be improved by encouraging collaboration between competing groups.
According to Nicholas Reich, biostatistician at UMass, "Every year the Centers for Disease Control hosts a flu forecasting challenge. It's the most organized and public effort at forecasting any infectious disease anywhere in the world. Our lab is now in our third year of participating. This year, we wanted to take it to the next level, so we worked with other teams year-round to develop a way that our models could work together to make a single best forecast for influenza."
Reich and colleagues at UMass collaborated with teams at Carnegie Mellon University, Columbia University, and a group at Los Alamos National Laboratory in a group called the FluSight Network. It issues a new flu season forecast every Monday for public health researchers and practitioners that compares the flu trajectory this year to past years.
In a recent publication, Reich and colleagues state that their aim is to "combine forecasting models for seasonal influenza in the U.S. to create a single ensemble forecast. The central question is, can we provide better information to decision makers by combining forecasting models and specifically, by using past performance of the models to inform the ensemble approach."
The team is now submitting forecasts from their best performing model and are posting them once a week this season to the CDC's 2017-18. FluSight Challenge Reich estimates that there are about 20 teams this year participating in the CDC challenge nationwide, who produce about 30 different models. Each model forecasts the onset of the flu season, how it will progress over the coming few weeks, when it will peak, and how intense the peak will be compared to other seasons.
The public health effort to improve flu season forecasts is relatively recent, Reich says. "There has been tremendous progress in how we think about infectious disease forecasting in just the last five years," he notes. "If you compare that to something like weather forecasting, which has been going on for decades, we're in the middle of a long process of learning and improvement. Someday we might be able to imagine having a flu forecast on our smart phones that tells us, for example, it's an early season and I'd better get Mom to the clinic to get her vaccination early this year. We're close, but that's not here quite yet."