Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 8 , Issue 2 , PP: 37-45, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ehsaneh khodadadi 1 *
Doi: https://doi.org/10.54216/JAIM.080205
In this review paper, the authors discuss the development and application of methods for modeling and control and comparison of viral spreading in society with fractional-order and ML techniques for data analysis. Some of the most well-known epidemiological models are based on traditional approaches to describing disease diffusion and often need to be more sufficient when mapping the realistic disease distribution. However, fractional-order models give more flexibility and accuracy due to the memory incorporated and interaction factors. Moreover, the amalgamation of ML and artificial intelligence allows the analysis of considerable and heterogeneous amounts of data, enabling real-time prediction and favorable outbreak response measures. This paper outlines some benefits of integrating these sophisticated techniques while discussing issues such as the quality of inputs, problems in the methods deployed, and issues of visibility of the methods deployed. Finally, it proposes better epidemic preparedness and response through interdisciplinary approaches that emphasize the role of these technologies in a society that is more vulnerable to epidemic diseases.
Epidemiological modeling , fractional Order models , Artificial intelligence , Viral outbreaks , Public health and predictive modeling
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