This article presents a comprehensive evaluation and ranking of various computer-aided methods for detecting epidemiological threats, utilizing Pareto optimization to balance multiple performance criteria. These methods are assessed based on their accuracy, speed of operation, scalability, and implementation costs. Computer-aided methods have emerged as powerful tools, leveraging advanced algorithms and computational capabilities to enhance detection processes. Pareto optimization plays a significant role in multi-criteria decision-making, allowing for the balancing of conflicting objectives such as maximizing detection accuracy while minimizing costs and processing times. Extensive simulations were conducted to model various outbreak scenarios, including different types of infectious diseases and spread rates. These simulations validated the effectiveness of each method under controlled conditions, providing insights into their practical applications. The performance metrics recorded during these simulations informed the ranking of the methods. The results of the ranking are presented in a detailed table, with each method's performance metrics clearly outlined. The findings indicate that while no single method excels in all criteria, certain approaches demonstrate superior performance in specific contexts. In contrast, traditional mathematical models were more scalable and cost-effective but less accurate in rapidly changing scenarios. The discussion delves into the advantages and disadvantages of each method. Mathematical models, while cost-effective and scalable, are often rigid and less adaptable to new or unforeseen variables.