The future of influenza forecasts

Recent years have seen a growing interest in generating real-time epidemic forecasts to help control infectious diseases, prompted by a succession of global and regional outbreaks. Increased availability of epidemiological data and novel digital data streams such as search engine queries and social media (1, 2), together with the rise of machine learning and sophisticated statistical approaches, have injected new blood into the science of outbreak forecasts (3, 4). In parallel, mechanistic transmission models have benefited from computational advances and extensive data on the mobility and sociodemographic structure of human populations(5, 6). In this rapidly advancing research landscape, modeling consortiums have generated systematic model comparisons of the impact of new interventions and ensemble predictions of outbreak trajectory, for use by decision makers (7—12). Despite the rapid development of disease forecasting as a discipline, however,and the interest of public health policy makers in making better use of analytics tools to control outbreaks,forecasts are rarely operational in the same way that weather forecasts, extreme events, and climate predictions are. The influenza study by Reich et al. (13) inPNAS is a unique example of multiyear infectious disease forecasts featuring a variety of modeling approaches,with consistent model formulations and forecasting targets throughout the 7-y study period (13). This is a major improvement over previous model comparison studies that used different targets and time horizons and sometimes different epidemiological datasets.