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Google unveils PPE for automated geographic forecasting Google unveiled its Planetary Prediction Engine (PPE) on August 27. The system receives a textual task and labeled source data, then produces a geographic forecast. It is designed to automate the data‑gathering and preparation steps that epidemiologists normally perform manually. According to the technical preprint, preparing data for an operational epidemic forecast involves more than >700 actions. PPE first defines the target area and time window and decides how to link tables. It then searches open geographic databases, government portals and scientific repositories, harmonizes the information to common district boundaries and assembles a training set. Using that training set, PPE builds a forecast and tests it on held‑out districts and weeks. During the Bundibugyo ebolavirus outbreak in the Democratic Republic of Congo, the system ranked districts each week before any cases were reported. Over five weekly forecasts it placed 15 of 18 districts that later saw their first cases inside its top‑ten risk list, achieving 83.3% accuracy. The authors compare this result with the approximately ~73% performance of an earlier published model that also ranked ten districts by risk. PPE separates training from verification; its Feature Gate filter removes variables that would pre‑reveal the answer, such as part of the target indicator, same‑questionnaire data, event consequences or future information. Missing values are imputed only on the training 🔗 Read original →