  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.In this paper, we
propose to augment epidemiological forecasting with actual data-driven models
that will learn to fine-tune predictions for different contexts (e.g., per
country). We have therefore built a pandemic simulation and forecasting toolkit
that combines a deep learning estimation of the epidemiological parameters of
the disease in order to predict the cases and deaths, and a genetic algorithm
component searching for optimal trade-offs/policies between constraints and
objectives set by decision-makers. Replaying pandemic evolution in various
countries, we experimentally show that our approach yields predictions with
much lower error rates than pure epidemiological models in 75% of the cases and
achieves a 95% R2 score when the learning is transferred and tested on unseen
countries. When used for forecasting, this approach provides actionable
insights into the impact of individual measures and strategies.
