Deep Learning Models for Predicting and Managing Airborne Diseases in Polluted Areas.

Sandhya Prajapati , Manjunath Gowda , Santosh Kumar Singh , Amit Kumar Pandey

Airborne disease prediction, deep learning, LSTM, Spatial-Temporal Graph Neural Network (STGN), pollution, hybrid models, long-term forecasting.

Airborne illnesses present considerable public health challenges, especially in areas with high levels of pollution where environmental elements heighten the risk of disease spread. Effective long-term predictions are essential for reducing outbreaks and enacting preventive strategies. In this research, we introduce a deep learning framework that combines Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Neural Networks (STGNs) to analyze the dissemination of airborne diseases affected by pollution levels. Furthermore, we investigate two hybrid models that integrate LSTM with STGN and Transformer-based frameworks. Our model projects disease patterns up to 35 years ahead, utilizing historical epidemiological and environmental information. The findings highlight the advantages of our hybrid method in effectively capturing both temporal relationships and spatial dependencies, resulting in marked enhancements in predictive accuracy.

Published Paper   E-Certificate

"Deep Learning Models for Predicting and Managing Airborne Diseases in Polluted Areas.", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b276-b285, March-2025, Available :https://ijsdr.org/papers/IJSDR2503131.pdf

Volume 10 Issue 3, March-2025

Pages : b276-b285

Paper Reg. ID: IJSDR_301057

Published Paper Id: IJSDR2503131

Downloads: 000147

Research Area: Science All

Country: Mumbai, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2503131

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2503131

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition