CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
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Abstract
Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields like disease outbreak prediction is paramount. Deep neural networks are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for Continual Learning by leveraging domain adaptation via Elastic weight consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher information matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to essential parameters. We conducted experiments on three distinct diseases, influenza, mpox, and measles, with customized metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts. The results indicate that CEL adapts well to incremental data. CEL's robustness emphasizes its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction by addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate and timely predictions.
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Memory-preserving mechanisms like Elastic Weight Consolidation are important in continual learning.
“it demonstrated the importance of memory-preserving mechanisms like Elastic Weight Consolidation in continual learning [54].”
From GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis (2025) · cites this paper as [54]
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Huang, H., Ye, X., Mustafa, M. B., Dong, Q., Li, Y., Asemi, A., & Asemi, A. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02086-2
- Authors
- Aslam, S., Rasool, A., Li, X., & Wu, H.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wu, Hongyan
- Venue
- Interdisciplinary Sciences: Computational Life Sciences
- Published
- 2025-02-28
- Volume
- 17
- Issue
- 2
- Pages
- 390-408
- ISSN
- 1867-1462 (ISSN-L)
- 1913-2751
- OpenAlex
- W4408051174
- Cited by
- 23
- References
- 4
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 4
- FWCI
- 7.69
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (62273322)
- Natural Science Foundation of Guangdong Province (2024A1515012269)
- Shenzhen Science and Technology Program (KQTD20190929172835662)
- Shenzhen Science and Technology Innovation Program (CJGJZD20220517142000002)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- continual learning, epidemic forecasting, catastrophic forgetting, elastic weight consolidation, domain adaptation, Fisher information matrix, incremental learning, influenza, monkeypox, measles, memory stability
- MeSH terms
- Machine Learning, Disease Outbreaks, Humans, Influenza, Human, Measles, Neural Networks, Computer
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