Medical Express  September 16, 2020 Researchers at the University of Georgia used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. They challenged their algorithm to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Their method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, the model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. They successfully applied the detection algorithm […]