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Predicting Trends of Coronavirus Disease (COVID19) Using SIRD and Gaussian-SIRD Models

EasyChair Preprint no. 4587

8 pagesDate: November 17, 2020


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.

Keyphrases: coronavirus disease, COVID-19, epidemiological model, Gaussian-SIRD model, outbreak model, SIRD model

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ahmad Sedaghat and Shahab S. Band and Amir Mosavi and Laszlo Nadai},
  title = {Predicting Trends of Coronavirus Disease (COVID19) Using SIRD and Gaussian-SIRD Models},
  howpublished = {EasyChair Preprint no. 4587},

  year = {EasyChair, 2020}}
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