Scarpino SV, Scott JG, Eggo RM, Clements B, Dimitrov NB, Meyers LA (2020) Socioeconomic bias in influenza surveillance. PLoS Comput Biol 16(7): e1007941. https://doi.org/10.1371/journal.pcbi.1007941
Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.