When will the Covid-19 epidemic fade out?
Renna, I.
Show abstract
A discrete-time deterministic epidemic model is proposed with the aim of reproducing the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model under the framework of a small world network formulation. Using this model, we make predictions about the peak of the Covid-19 epidemic in Italy. A Gaussian fit is also performed, to make a similar prediction.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Solvable delay model for epidemic spreading: the case of Covid-19 in Italy 97%
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 97%
- Easing COVID-19 lockdown measures while protecting the older restricts the deaths to the level of the full lockdown 96%
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 97%
- Prediction of the time evolution of the Covid-19 Pandemic in Italy by a Gauss Error Function and Monte Carlo simulations 96%
- A novel deterministic forecast model for the Covid-19 epidemic based on a single ordinary integro-differential equation 95%
Similar papers in this journal
Similar papers in this journal
- Modelling information-dependent social behaviors in response to lockdowns: the case of COVID-19 epidemic in Italy 97%
- Estimating the state of the Covid-19 epidemic in France using a non-Markovian model 96%
- The trade-off between mobility and vaccination for COVID-19 control: a metapopulation modeling approach 96%
"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.