Deep Learning and Constrained Optimization for Epidemic Control

The SARS-CoV-2 pandemic has galvanized the interest of the scientific community. Particular interest has been posed on methodologies apt at predicting the trend of the epidemiological curve, i.e. the daily number of infected individuals in the population. In this work, we argue for a model capable of producing intervention plans focused on counteracting the negative effects of an outbreak, with real applications on the ongoing pandemic. To do so, we relied on the use of Machine Learning models and Combinatorial Optimization approaches. The project entails the development of a new predictive model capable of forecasting the number of infected individuals depending on non-pharmaceutical interventions. The development of the model required the use of state-of-the-art techniques, which relied on prior knowledge injection to guide the training process. The model is then used to boost a combinatorial process effectively producing the intervention plan. The ultimate result is a working prototype of a Decision Support System capable of assisting policy-makers during a virus outbreak.