Leveraging Machine Learning for Localized Disease Prediction and Alert System: A Comprehensive Approach to Public Health Monitoring and Outbreak Response

Viruses pose a serious threat to public health because of their fast spread, which makes disease control a huge global problem. Diseases can be spread through direct touch, airborne particles, tainted food and drink, and vectors like mosquitoes. Global connection has made it easier for major diseases to spread in both wealthy and underdeveloped nations. Environmental factors such as urbanization, climate change, and poor sanitation enhance the spread of disease. The global health system is challenged by the dynamic risks of new pathogens. Early warnings in case of a spread of disease can reduce its effects on public health by acting swiftly. A subset of artificial intelligence known as machine learning is vital in outbreak forecasting because it can analyze immense amounts of data and predict disease spread. To track disease transmission dynamics and provide timely alert systems for public health action, this study explores how ML techniques can be utilized in the analysis of data from different sources, including social media, climate data, and medical records. ML combined with social media data can improve outbreak response and public health monitoring. The proposed approach, which includes data preprocessing followed by PCA for dimension reduction, ResNet for prediction, and RMSprop for optimizing, is proven to predict disease outbreaks using machine learning. Thus, the results depict that timely preventive measures are facilitated by the alerts and insights coming from timely machine learning models, thus greatly lessening the impact of disease epidemics.