The current COVID-19 wave will likely be mitigated in the second-line European countries

Samuel Soubeyrand; Melina Ribaud; Virgile Baudrot; Denis Allard; Denys Pommeret; Lionel Roques

COVID-19 currently generates a major pandemic that has caused about 125 000 registered deaths across the world by April 14, 2020. From reported mortality data at the scale of countries (Dong et al., 2020) , one observes a large diversity of temporal dynamics in terms of, for instance, outbreak start, acceleration, epidemic peak and plateau. This diversity can be reproduced, at least partially, by epidemiological models. Either parsimonious (Roques et al., 2020) or more complex Prem et al., 2020) , such models have already been run to describe, infer and forecast the epidemics and estimate epidemiological parameters (e.g., the basic reproduction number R 0 and the death rate). These epidemiological models, based on SIR (Susceptible-Infected-Removed) architectures or their extensions, generally include compartments corresponding to the dead fraction of the population and can hence be used to model and predict the temporal evolution of mortality due to COVID-19, in which we are interested in here. Data-driven approaches have also been proposed, grounded for instance on simple regressions (Zhao et al., 2020) , artificial intelligence-inspired methods (Hu et al., 2020) as well as coupled SIR-neural network approaches (Zeng et al., 2020) . However, in all these cases, the training data correspond to the past dynamics in the country of interest and therefore cannot take into account processes that only arise when a certain number of deaths is reached (e.g., saturation of medical structures or lockdown).