Transformative Epidemic Forecasting: Integrating AI and ML For Predictive Analytics and User Awareness in Mobile Applications

DOI

10.2991/978-94-6463-858-5_40How to use a DOI?

Keywords

Epidemic curve; health data analytics; forecast validation; data-driven outbreak detection; infectious disease forecasting; public health monitoring; and disease outbreak prediction

Abstract

Epidemic outbreaks present great challenges to public health systems, often leading to very significant social and economic issues. Thus, timely predictions help relevant authorities to take proactive measures to minimize the impact of such epidemics. This study proposes an interactive epidemic outbreak forecasting system that will integrate modern machine learning models and a user-friendly mobile app to enhance public health preparedness. Historical, clinical and geographic data, covering the years 2020 to 2022, have been collected from credible sources such as data.gov.in and Kaggle. The dataset was subdivided into training data for 2020-2021 and testing data for 2022, which allows the compilation of a comprehensive report card of the models. The paper uses three models: Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to forecast disease outbreaks. Most competitive research indicates the GRU model gives the most reasonable and efficient trade-off in computational efficiency and predictive power with a Mean Absolute Percentage Error (MAPE) of 32.46%. The mobile application allows users to take preventive action based on interactive visualizations, awareness content about their health, and timely notices on outbreaks. This study shows how technology focused on the user can be blended and adapted nicely with predictive analytics to build preparedness against epidemics and reduce the burden of communicable diseases.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Afsar
AU  - Hajira Shuhaila
AU  - P. Hanna
AU  - K. P. Ayra Riyaz
AU  - P. A. Hannathu Nishana
AU  - Shanid Malayil
AU  - A. K. Mubeena
PY  - 2025
DA  - 2025/11/04
TI  - Transformative Epidemic Forecasting: Integrating AI and ML For Predictive Analytics and User Awareness in Mobile Applications
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 456
EP  - 469
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_40
DO  - 10.2991/978-94-6463-858-5_40
ID  - Afsar2025
ER  -