Data-driven Simulation and Optimization for Covid-19 Exit Strategies
- ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) : 3434-3442
Résumé
The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.
Mots-clés
COVID-19 pandemic, exit strategy, lockdown lifting, mitigation measures, epidemic modeling, parameter sensitivity, data-driven simulation, public health policy, post-pandemic recovery