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Sensitivity Analysis on Predictive Capability of SIRD Model for Coronavirus Disease (COVID-19)
  • +2
  • Ahmad Sedaghat,
  • Fadi Alkhatib,
  • Seyed Amir Abbas Oloomi,
  • Mahdi Ashtian Malayer,
  • Amir Mosavi
Ahmad Sedaghat
School of Engineering, Australian College of Kuwait
Fadi Alkhatib
School of Engineering, Australian College of Kuwait
Seyed Amir Abbas Oloomi
Department of Mechanical Engineering, Yazd Branch, Islamic Azad University
Mahdi Ashtian Malayer
Young Researchers and Elite Club, Yazd Branch, Islamic Azad University
Amir Mosavi
John von Neumann Faculty of Informatics, Obuda University, School of the Built Environment, Oxford Brookes University, School of Economics and Business, Norwegian University of Life Sciences
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Abstract

SIR model is one of the simplest methods used in prediction of endemic/pandemic outbreaks. We examined SIRD model for development of COVID-19 in Kuwait which was started on 24 February 2020 by 5 patients in Kuwait. This paper investigates sensitivity of SIRD model for development of COVID-19 in Kuwait based on duration of progressed days of data. For Kuwait, we have fitted SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed sensitivity of the model with number of days of data. The parameters of 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 SIRD model indicates that after 40 days the peak infectious day can be adequately predicted; althogh, error percentage from sensetivity analysis indicates 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 nonlinear dynamics of a pandemic. 2