Elementary time-delay dynamics of COVID-19 disease
Menendez, J.
Show abstract
An elementary model of COVID-19 dynamics--based on time-delay differential equations with a step-like survival function--is shown to be in good agreement with data from China and South Korea. The time-delal approach overcomes the major limitation of standard Susceptible-Exposed-Infected-Recovered (SEIR) models based on ordinary differential equations, namely their inability to predict the observed curve of infected individuals as a function of time. The model is also applied to countries where the epidemic is in earlier stages, such as Italy and Spain, to obtain estimates of the total number of cases and peak number of infected people that might be observed.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adding a reaction-restoration type transmission rate dynamic law to the basic SEIR COVID-19 model 96%
- Impact of exposure frequency on disease burden of the common cold - a mathematical modeling perspective 96%
- Modeling the initial phase of COVID-19 epidemic: The role of age and disease severity in the Basque Country, Spain 96%
Similar papers in this journal
Similar papers in this journal
- Estimation and optimal control of the multi-scale dynamics of the Covid-19 96%
- Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model 95%
- Analog of the Hutchinson equation in biophysical neurodynamics: from the Morris-Lecar model to a delay differential equation 95%
Similar papers in this journal
Similar papers in this journal
- A novel deterministic forecast model for the Covid-19 epidemic based on a single ordinary integro-differential equation 97%
- 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 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.