Pathology

Disease

Population

Coronavirus disease 2019 (COVID-19)

Mathematics

Infectious disease (medical specialty)

Pandemic

Statistics

Distribution (mathematics)

Outbreak

Mathematical analysis

Econometrics

Log-normal distribution

Virology

Epidemic model

Quarantine

2019-20 coronavirus outbreak

Computer science

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Medicine

Environmental health

Chaos An Interdisciplinary Journal of Nonlinear Science

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 and the economical and social textures, France and Italy governments have partially released lockdown measures. Here, we extrapolate the long-term behavior of the epidemic in both countries using a susceptible-exposed-infected-recovered model, where parameters are stochastically perturbed with a lognormal distribution to handle the uncertainty in the estimates of COVID-19 prevalence and to simulate the presence of super-spreaders. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemic leading or not to a second wave of infections. Furthermore, the presence of super-spreaders adds instability to the dynamics, making the control of the epidemic more difficult. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of the order of 10×106 units in both countries.

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