Investigating Epidemic Growth of COVID-19 in Saudi Arabia based on Time Series Models

Predictive mathematical models for simulating the spread of the COVID-19 pandemic are an interesting and fundamental approach to understand the infection growth curve of the epidemic and to plan effective control strategies. Time series predictive models are one of the most important mathematical models that can be utilized for studying the pandemic growth curve. In this study, three-time series models (Susceptible-Infected-Recovered-Death (SIRD) model, Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model, and Susceptible-Exposed-Infected-Quarantine-Recovered-Death-Insusceptible, (SEIQRDP) model) have been investigated and simulated on a real dataset for investigating Covid-19 outbreak spread in Saudi Arabia. The simulation results and evaluation metrics proved that SIRD and SEIQRDP models provided a minimum difference error between reported data and fitted data. So using SIRD, and SEIQRDP models are used for predicting the pandemic end in Saudi Arabia. The prediction results showed that the Covid-19 growth curve will be stable with detected zero active cases on 2 February 2021 according to the prediction computations of the SEIQRDP model. Also, the prediction results based on the SIRD model showed that the outbreak will be stable with active cases after July 2021.

@article{Torky2020,
  title     = {Investigating Epidemic Growth of COVID-19 in Saudi Arabia based on Time Series Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Torky and M. Sh Torky and Azza Ahmed and Aboul Ella Hassanein and Wael Said},
  doi       = {10.14569/IJACSA.2020.0111256},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111256}
}