COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, has impacted over 200 countries globally.
• Mathematical epidemic models are crucial for predicting disease spread and informing control strategies.
• Network-based epidemic modeling, considering social contact, is increasingly utilized.

Purpose of the Study:

• To propose and utilize a network-based epidemic model for simulating COVID-19 spread in Saudi Arabia.
• To evaluate the effectiveness of control measures implemented by the Saudi government.
• To predict future disease dynamics under various scenarios and assess vaccination strategies.

Main Methods:

• Development of a contact network model reflecting realistic social behaviors in Saudi Arabia.
• Simulation of COVID-19 transmission dynamics using the network model.
• Analysis of different intervention strategies, including social distancing, school/mosque closures, and vaccination.

Main Results:

• Without control measures, Saudi Arabia's COVID-19 outbreak peak was projected for April 21, 2020, with ~26 million infections.
• Social distancing was identified as a critical factor in managing epidemic dynamics.
• Closure of schools and mosques demonstrated the most significant impact on delaying the peak and reducing infection rates.

Conclusions:

• Implemented control measures were vital in mitigating the COVID-19 outbreak in Saudi Arabia.
• Social distancing and targeted closures of public spaces are effective strategies for epidemic control.
• Vaccination, if widely adopted (70% coverage), could rapidly conclude the epidemic.