Project R-11451

Policy support for managing the COVID pandemic through artificial intelligence (Research)

Epidemiological modeling recently made important progress. The large variety of models allow for simulations, which can be combined with advanced optimization approaches using artificial intelligence, in order to identify the most suitable prevention and containment measures. We will extend state-of-the-art Reinforcement Learning (RL) techniques, which have been shown to outperform the currently used techniques by epidemiologists, and can deal well with uncertainties. Next to epidemic factors, health (e.g. hospital load and death counts) and economic factors will be included. It is clear from cognitive sciences, that the way people and groups react to the epidemic itself, and to the prevention and containment measures, has a big impact on how the epidemic evolves. By taking these cognitive variables into account the impact assessment and choice of optimal measures will be improved. We allow for multi-criteria optimization, such that policy makers can trade-off different aspects by simulating and assessing the potential impact of each measure. We will also pay attention to the communication of the outcome of the learning process to the user, by building upon research on explainable RL. This way the user better understands why certain simulations have the impact that they have. The research will form the basis for a valuable interactive tool for decision makers for the current COVID-19 pandemic even when information on epidemics only gradually becomes available.

01 November 2020 - 31 October 2021