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A Study of Data-Driven Methods for Adaptive Forecasting of COVID-19 Cases

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14254))

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Abstract

Severe acute respiratory disease SARS-CoV-2 has had a profound 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.

This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE - TEAMING) and from the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. It was also supported by the CIPHIS (Cyprus Innovative Public Health ICT System) project of the NextGenerationEU programme under the Republic of Cyprus Recovery and Resilience Plan under grant agreement C1.1l2.

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Authors and Affiliations

  1. KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus

    Charithea Stylianides, Kleanthis Malialis & Panayiotis Kolios

Authors
  1. Charithea Stylianides
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  2. Kleanthis Malialis
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  3. Panayiotis Kolios
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Correspondence to Kleanthis Malialis .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Stylianides, C., Malialis, K., Kolios, P. (2023). A Study of Data-Driven Methods for Adaptive Forecasting of COVID-19 Cases. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-44207-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44206-3

  • Online ISBN: 978-3-031-44207-0

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