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Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory

Author

Listed:
  • Finn Stevenson

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
    These authors contributed equally to this work as co-first authors.)

  • Kentaro Hayasi

    (School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2050, South Africa
    These authors contributed equally to this work as co-first authors.)

  • Nicola Luigi Bragazzi

    (Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Jude Dzevela Kong

    (Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Ali Asgary

    (Disaster & Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-Response Simulation, York University, Toronto, ON M3J 1P3, Canada)

  • Benjamin Lieberman

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Xifeng Ruan

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Thuso Mathaha

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Salah-Eddine Dahbi

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Joshua Choma

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Mary Kawonga

    (Department of Community Health, School of Public Health, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • Mduduzi Mbada

    (Office of the Premier, Gauteng Government, 13th Floor, East Wing, 30 Simmonds St., Marshalltown, Johannesburg 2107, South Africa)

  • Nidhi Tripathi

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa)

  • James Orbinski

    (Dahdaleh Institute for Global Health Research, York University, Toronto, ON M3J 1P3, Canada)

  • Bruce Mellado

    (School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
    iThemba LABS, National Research Foundation, P.O. Box 722, Somerset West 7129, South Africa
    These authors contributed equally to this work as co-last authors.)

  • Jianhong Wu

    (Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
    These authors contributed equally to this work as co-last authors.)

Abstract

The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.

Suggested Citation

  • Finn Stevenson & Kentaro Hayasi & Nicola Luigi Bragazzi & Jude Dzevela Kong & Ali Asgary & Benjamin Lieberman & Xifeng Ruan & Thuso Mathaha & Salah-Eddine Dahbi & Joshua Choma & Mary Kawonga & Mduduzi, 2021. "Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7376-:d:591815
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    References listed on IDEAS

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    Cited by:

    1. Jiecheng Song & Guanchao Tong & Wei Zhu, 2023. "A Detecting System for Abrupt Changes in Temporal Incidence Rate of COVID-19 and Other Pandemics," Stats, MDPI, vol. 6(3), pages 1-11, September.

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