Reemerging & Emerging Epidemics Monitoring via Educational Outreach & Safe Data in Sustainability

Social networks are taking over daily life, together with smart data and linked objects (IoMT). Their massive information generation makes it more difficult to capture secure and pertinent content during emergencies, such as the reemerging and emergence of infectious and viral diseases (REIVD). A multimodal deep learning (DL) method to health monitoring is the main goal of this study. It uses CNN-LSTM (Deep Convolutional Neural Networks-Long Short-Term Memory) to lessen REIVD epidemics. Through the use of smart data, connected devices (IoMT), and various social media platforms, this model seeks to efficiently gather safe and pertinent content in order to prevent REIVD and promote sustainability. In order to reduce these important dangers, what steps can we take? The threats posed by REIVD include COVID-19, Polio, Cholera, Syphilis, Tuberculosis, and so on. The outcomes are evaluated against alternative methods using a publicly accessible dataset, demonstrating commendable performance with Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and an R Squared of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5. 2 8 4 6 5 6 \%}$</tex>.