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Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19

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Engineering Applications of Neural Networks (EANN 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1600))

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Abstract

The proportional hazard Cox model is traditionally used in survival analysis to estimate the effect of several variables on the hazard rate of an event. Recently, neural networks were proposed to improve the flexibility of the Cox model. In this work, we focus on an extension of the Cox model, namely on a non-proportional relative risk model, where the neural network approximates a non-linear time-dependent risk function. We address the issue of the lack of time-varying variables in this model, and to this end, we design a deep neural network model capable of time-varying regression.

The target application of our model is the waning of post-vaccination and post-infection immunity in COVID-19. This task setting is challenging due to the presence of multiple time-varying variables and different epidemic intensities at infection times. The advantage of our model is that it enables a fine-grained analysis of risks depending on the time since vaccination and/or infection, all approximated using a single non-linear function. A case study on a data set containing all COVID-19 cases in the Czech Republic until the end of 2021 has been performed. The vaccine effectiveness for different age groups, vaccine types, and the number of doses received was estimated using our model as a function of time. The results are in accordance with previous findings while allowing greater flexibility in the analysis due to a continuous representation of the waning function.

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Notes

  1. 1.

    https://github.com/havakv/pycox.

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Acknowledgments

Roman Neruda, Petra Vidnerová and Gabriela Suchopárová acknowledge the institutional support of the Institute of Computer Science, Czech Academy of Sciences RVO 67985807. This research was partially supported by the SVV project number 260 575. Gabriela Suchopárová was supported by Charles University Grant Agency project no. 246322.

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Authors and Affiliations

  1. Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic

    Gabriela Suchopárová, Petra Vidnerová & Roman Neruda

  2. Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Gabriela Suchopárová

  3. Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic

    Martin Šmíd

  4. Centre for Modelling of Biological and Social Processes, Prague, Czech Republic

    Gabriela Suchopárová, Petra Vidnerová, Roman Neruda & Martin Šmíd

Authors
  1. Gabriela Suchopárová
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  2. Petra Vidnerová
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  3. Roman Neruda
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  4. Martin Šmíd
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Corresponding author

Correspondence to Roman Neruda .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Anastasios Tefas

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Suchopárová, G., Vidnerová, P., Neruda, R., Šmíd, M. (2022). Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-08223-8_26

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  • Online ISBN: 978-3-031-08223-8

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