Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care

Area of Science:

• Epidemiology
• Public Health
• Computational Intelligence

Background:

• COVID-19 poses a significant global health challenge due to its contagiousness and evolving nature.
• The absence of vaccines and effective treatments necessitates advanced predictive models for public health management.
• Intelligent computing, including AI and machine learning, is crucial for developing smart healthcare solutions.

Purpose of the Study:

• To propose a predictive model for COVID-19 spread in KSA using the SIR model and machine learning.
• To forecast the pandemic's trajectory for 700 days, predicting its long-term persistence or decline.
• To evaluate the impact of interventions like lockdowns and new medicines on disease spread.

Main Methods:

• Utilized the Susceptible-Infected-Recovered (SIR) epidemiological model.
• Integrated machine learning techniques for enhanced predictive accuracy.
• Conducted simulations for three scenarios: no actions, lockdown, and new medicines.

Main Results:

• Lockdowns were found to delay the infection peak and alter the infected curves.
• New medicines significantly reduced the number of infected individuals over time.
• Simulations predicted the highest case levels between November 15-30, 2020, with full control potentially not achieved until June 2021.
• The reproductive rate indicated that lockdowns and isolation alone are insufficient to halt the pandemic.

Conclusions:

• A strict, long-term containment strategy is recommended for successful epidemic control.
• Intelligent computing models are vital for smart healthcare services and citizen well-being during pandemics.
• Mathematical modeling and simulations provide critical insights into pandemic dynamics and intervention effectiveness.