Severe acute respiratory disease SARS-CoV-2 has had a found impact on public health systems and healthcare emergency response especially with respect to making decisions on the most effective measures to be taken at any given time. As demonstrated throughout the last three years with COVID-19, the prediction of the number of positive cases can be an effective way to facilitate decision-making. However, the limited availability of data and the highly dynamic and uncertain nature of the virus transmissibility makes this task very challenging. Aiming at investigating these challenges and in order to address this problem, this work studies data-driven (learning, statistical) methods for incrementally training models to adapt to these nonstationary conditions. An extensive empirical study is conducted to examine various characteristics, such as, performance analysis on a per virus wave basis, feature extraction, "lookback" window size, memory size, all for next-, 7-, and 14-day forecasting tasks. We demonstrate that the incremental learning framework can successfully address the aforementioned challenges and perform well during outbreaks, providing accurate predictions.

翻译：严重急性呼吸综合征SARS-CoV-2对公共卫生系统和医疗应急响应产生了深远影响，尤其在决定任何特定时间应采取最有效措施方面。正如过去三年COVID-19疫情所展示，阳性病例数量的预测可有效促进决策制定。然而，有限的数据可用性以及病毒传播的高度动态性和不确定性使这一任务极具挑战性。为探讨这些挑战并解决该问题，本研究系统分析了数据驱动（学习与统计）方法，旨在通过增量式模型训练适应非平稳条件。我们开展了一项广泛的实证研究，考察了多种特征，包括基于病毒波次的性能分析、特征提取、“回溯”窗口大小、记忆容量等，所有评估均针对次日、7天及14天预测任务展开。研究证明，增量学习框架能成功应对上述挑战，并在疫情暴发期间表现优异，提供准确预测。