An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.

ABSTRACT: Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability a

SUBMITTER: Salman M 

PROVIDER: S-EPMC11573407 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature