Modelling the second wave of COVID-19 infections in France and Italy via a Stochastic SEIR model

TL;DR

This paper models the second wave of COVID-19 in France and Italy using a stochastic SEIR model to account for uncertainties, revealing how parameter variability can lead to different epidemic outcomes.

Contribution

It introduces a stochastic SEIR model to analyze COVID-19 resurgence, highlighting the impact of uncertainties on epidemic predictions.

Findings

Uncertainties in parameters cause rapid propagation of outcomes.

Prevalence estimates can fluctuate by tens of millions.

Model predicts potential for second wave depending on initial conditions.

Abstract

COVID-19 has forced quarantine measures in several countries across the world. These measures have proven to be effective in significantly reducing the prevalence of the virus. To date, no effective treatment or vaccine is available. In the effort of preserving both public health as well as the economical and social textures, France and Italy governments have partially released lockdown measures. Here we extrapolate the long-term behavior of the epidemics in both countries using a Susceptible-Exposed-Infected-Recovered (SEIR) model where parameters are stochastically perturbed to handle the uncertainty in the estimates of COVID-19 prevalence. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemics leading or not to a second wave of infections. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of order of ten millions units in both countries.