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Continual learning, the ability of a model to learn over time withoutforgetting previous knowledge and, therefore, be adaptive to new data, isparamount in dynamic fields such as disease outbreak prediction. Deep neuralnetworks, i.e., LSTM, are prone to error due to catastrophic forgetting. Thisstudy introduces a novel CEL model for continual learning by leveraging domainadaptation via Elastic Weight Consolidation (EWC). This model aims to mitigatethe catastrophic forgetting phenomenon in a domain incremental setting. TheFisher Information Matrix (FIM) is constructed with EWC to develop aregularization term that penalizes changes to important parameters, namely, theimportant previous knowledge. CEL's performance is evaluated on three distinctdiseases, Influenza, Mpox, and Measles, with different metrics. The highR-squared values during evaluation and reevaluation outperform the otherstate-of-the-art models in several contexts, indicating that CEL adapts toincremental data well. CEL's robustness and reliability are underscored by itsminimal 65% forgetting rate and 18% higher memory stability compared toexisting benchmark studies. This study highlights CEL's versatility in diseaseoutbreak prediction, addressing evolving data with temporal patterns. It offersa valuable model for proactive disease control with accurate, timelypredictions.

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation