Computer models, including SIR and Agent-Based Models, have been crucial in comprehending COVID-19 transmission dynamics and developing containment strategies.
Computer models like SIR and Agent-Based Models are crucial for comprehending COVID-19 transmission dynamics and developing containment strategies.
The COVID-19 outbreak demonstrated the significance of computer models in comprehending the complex dynamics of disease transmission. Epidemiological models have played a crucial role in understanding the complexities of COVID-19, developing effective strategies for its containment, and supporting theories regarding its transmission. The SIR (Susceptible-Infectious-Recovered) paradigm classifies people into 3 separate categories: risky, infectious, and healed. Agent-Based Models (ABM) are advanced frameworks to depict actions and relation-ships of people within a community including their various de-mographics and habits. The models have been utilized to analyze the impact of COVID-19 on specific locations, healthcare systems, and the efficacy of various treatments. However, they may face difficulties in specific situations because to the need for significant computing power and information for optimization. Computer models have been useful in studying disease transmission and creating effective antiviral therapies.
Chakroun et al. (Tue,) conducted a review in COVID-19. Monte Carlo-Based Simulations and Agent-Based Models was evaluated. Computer models, including SIR and Agent-Based Models, have been crucial in comprehending COVID-19 transmission dynamics and developing containment strategies.
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