Predicting Trends of Coronavirus Disease (COVID19) Using SIRD and Gaussian-SIRD Models
Sedaghat, A.; MOSAVI, A.
Show abstract
Eruption of COVID-19 patients in 215 countries worldwide have urged for robust predictive methods that can detect as early as possible size and duration of the contagious disease and also providing precision predictions. In many recent literatures reported on COVID-19, one or more essential parts of such investigation were missed. One of crucial elements for any predictive method is that such methods should fit simultaneously as many data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases and deceased cases and so on. Other crucial elements include sensitivity and precision of such predictive methods on amount of data as the contagious disease evolved day by day. To show importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on development of COVID-19 in Kuwait. It is observed that SIRD model quickly pick up main trends of COVID-19 development; but Gaussian-SIRD model provides precise prediction at longer period of time.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 97%
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 96%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 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.