Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• The COVID-19 pandemic significantly impacted global health and economies.
• Saudi Arabia implemented strict control measures, including curfews and restrictions on religious gatherings, to curb the virus's spread.
• Assessing the effectiveness of these measures is crucial for future pandemic preparedness.

Purpose of the Study:

• To evaluate the impact of the COVID-19 curfew in Saudi Arabia on disease transmission.
• To compare the forecasting accuracy of the STARIMA and ARIMA statistical models in predicting COVID-19 cases.
• To determine the preferred model for analyzing epidemic trends post-curfew.

Main Methods:

• Utilized Autoregressive Integrated Moving Average (ARIMA) and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) models.
• Analyzed COVID-19 confirmation case data from Makkah, Jeddah, and Taif in Saudi Arabia.
• Data collection period: May 31 to October 11, 2020.

Main Results:

• STARIMA models demonstrated superior reliability in forecasting future COVID-19 epidemics compared to ARIMA models.
• The study highlighted the preference for STARIMA models during the period when the curfew was lifted.
• STARIMA models proved more effective in capturing spatial and temporal dynamics of COVID-19 spread.

Conclusions:

• STARIMA models offer a more accurate approach to predicting COVID-19 outbreaks in Saudi Arabia.
• The findings support the use of advanced statistical modeling for public health surveillance and response.
• Understanding the impact of interventions like curfews is vital for effective pandemic management.