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.

翻译：持续学习，即模型随时间学习而不遗忘先前知识并因此适应新数据的能力，在疾病爆发预测等动态领域中至关重要。深度神经网络（如LSTM）因灾难性遗忘而容易出错。本研究提出了一种新颖的CEL模型，通过利用弹性权重巩固（EWC）实现领域自适应进行持续学习。该模型旨在缓解领域增量设置下的灾难性遗忘现象。通过EWC构建Fisher信息矩阵（FIM），以开发正则化项，惩罚对重要参数（即重要的先前知识）的更改。CEL的性能在流感、猴痘和麻疹三种不同疾病上使用不同指标进行评估。在评估和重新评估过程中，高R²值在多个背景下优于其他最新模型，表明CEL能良好适应增量数据。CEL的稳健性和可靠性体现在其仅65%的最小遗忘率和相较于现有基准研究高18%的记忆稳定性。本研究强调了CEL在疾病爆发预测中的通用性，可处理随时间变化的时序模式数据，为主动疾病控制提供了具有准确及时预测能力的宝贵模型。