Analysis and Prediction of Viral Infections using Statistical Model Checking.

Building formal models for analyzing and forecasting infectious diseases is one of the important challenges for epidemics surveillance. In this paper, we propose a model-based approach that allows learning and analyzing viral infections behavior starting from the past observations using formal verification techniques. We first specify an automata-based model that epitomizes infections over time. Then, we use a statistical model checking to analyze the learned behavior and express probabilistic properties for providing reliable predictions that help to manage interventions and patient care. We experiment our approach with viral infections data provided by the Ministry of Health of Democratic Republic of the Congo.