A simple Covid-19 Epidemic Model and Containment Policy in France
Quadrat, J.-P.
Show abstract
We show that the standard SIR model is not effective to predict the 2019-20 coronavirus pandemic propagation. We propose a new model where the logarithm of the detected population number follows a linear dynamical system. We estimate the parameters of this system and compare models obtained with data observed from different countries. Based on the given estimator and results obtained with the Pr. Raoults treatment, we affirm with a reasonable degree of confidence that his "test-treat-noconfine" policy was less expensive in human lives than the"confine and wait for a proved treatment" policy adopted by the French government.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- An improved mathematical prediction of the time evolution of the Covid-19 Pandemic in Italy, with Monte Carlo simulations and error analyses 95%
- Prediction of the time evolution of the Covid-19 Pandemic in Italy by a Gauss Error Function and Monte Carlo simulations 95%
- A novel deterministic forecast model for the Covid-19 epidemic based on a single ordinary integro-differential equation 95%
Similar papers in this journal
- SARS-CoV-2 infection dynamics in Denmark, February through October 2020: Nature of the past epidemic and how it may develop in the future 95%
- Vaccination and Variants: retrospective model for the evolution of Covid-19 in Italy 95%
- Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts 95%
Similar papers in this journal
Similar papers in this journal
- Estimation and optimal control of the multi-scale dynamics of the Covid-19 96%
- Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model 94%
- Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.