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- W4386875902 abstract "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." @default.
- W4386875902 created "2023-09-20" @default.
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- W4386875902 date "2023-09-18" @default.
- W4386875902 modified "2023-10-16" @default.
- W4386875902 title "A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases" @default.
- W4386875902 doi "https://doi.org/10.48550/arxiv.2309.09698" @default.
- W4386875902 hasPublicationYear "2023" @default.
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