A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach

Noor Alkhateeb, Farag Sallabi, Saad Harous, Mamoun Awad

Research output: Contribution to journalArticlepeer-review


According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020.

Original languageEnglish
Article number4434
Issue number23
Publication statusPublished - Dec 2022
Externally publishedYes


  • COVID-19
  • COVID-19 outbreak
  • Monte Carlo
  • SIR models
  • time-dependent SIR model

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)


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