Building Early Warning Systems for Public Health Concerns Using AI-assisted Electrical Modelling for Epidemic Pattern Recognition

Abstract

A rapid recognition and handling of new threats to public health is crucial for reducing large-scale epidemic outbreaks as well as related consequences. However, this study is relevant because it could enhance the surveillance capabilities that can be used to respond swiftly and effectively to major outbreaks. While there are numerous challenges facing the use of artificial intelligence (AI) in epidemiological research, such technology has a lot of promise. Some of these include integration of complex data sources, validating data, managing computational requirements, and identifying and addressing privacy and security concerns No one doubts that Surveillance Predictive Modeling System-Based Healthcare Framework (SPMS-HF) will overcome these setbacks. SPMS-HF works by using potent AI algorithms to analyze electrical data and hence predict outbreak conditions. This allows for more accurate predictions and early warnings of potential public health risks. There could be different uses for SPMS-HF including real-time disease surveillance, resource efficiency, and public health. Implementation of this program enables healthcare givers alongside police officers to boost community health outcomes while improving their counter-response attitudes. To illustrate the applicability of SPMS-HF simulation analysis was carried out on historical epidemiological data. The results suggest that the model can identify possible health hazards as well as predict future outbreaks with accuracy These findings illustrate how e-images with AI can produce credible warning systems for public health.