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Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction

EasyChair Preprint no. 4597

5 pagesDate: November 18, 2020


Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

Keyphrases: compartmental model, coronavirus disease, COVID-19, epidemiological model, outbreak 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 Seyed Amir Abbas Oloomi and Mahdi Ashtian Malayer and Shahab S. Band and Amir Mosavi and László Nádai},
  title = {Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction},
  howpublished = {EasyChair Preprint no. 4597},

  year = {EasyChair, 2020}}
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