CEL: A Continual Learning Model for Disease Outbreak Prediction by
  Leveraging Domain Adaptation via Elastic Weight Consolidation
Saba Aslam Abdur Rasool Hongyan Wu Xiaoli Li
Continual learning, the ability of a model to learn over time without
forgetting previous knowledge and, therefore, be adaptive to new data, is
paramount in dynamic fields such as disease outbreak prediction. Deep neural
networks, i.e., LSTM, 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 important parameters, namely, the
important previous knowledge. CEL's performance is evaluated on three distinct
diseases, Influenza, Mpox, and Measles, with different metrics. The high
R-squared values during evaluation and reevaluation outperform the other
state-of-the-art models in several contexts, indicating that CEL adapts to
incremental data well. CEL's robustness and reliability are underscored by 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, addressing evolving data with temporal patterns. It offers
a valuable model for proactive disease control with accurate, timely
predictions.