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Article

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

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School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
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School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2050, South Africa
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Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
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Disaster & Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-Response Simulation, York University, Toronto, ON M3J 1P3, Canada
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Department of Community Health, School of Public Health, University of the Witwatersrand, Johannesburg 2050, South Africa
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Office of the Premier, Gauteng Government, 13th Floor, East Wing, 30 Simmonds St., Marshalltown, Johannesburg 2107, South Africa
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Dahdaleh Institute for Global Health Research, York University, Toronto, ON M3J 1P3, Canada
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iThemba LABS, National Research Foundation, P.O. Box 722, Somerset West 7129, South Africa
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Author to whom correspondence should be addressed.
These authors contributed equally to this work as co-first authors.
These authors contributed equally to this work as co-last authors.
Academic Editors: Xudong Huang and Oliver Faust
Int. J. Environ. Res. Public Health 2021, 18(14), 7376; https://doi.org/10.3390/ijerph18147376
Received: 3 May 2021 / Revised: 27 June 2021 / Accepted: 29 June 2021 / Published: 9 July 2021
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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. View Full-Text
Keywords: COVID-19; South Africa; early detection; crisis management; daily case prediction; Recurrent Neural Network with Long Short-Term Memory COVID-19; South Africa; early detection; crisis management; daily case prediction; Recurrent Neural Network with Long Short-Term Memory
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MDPI and ACS Style

Stevenson, F.; Hayasi, K.; Bragazzi, N.L.; Kong, J.D.; Asgary, A.; Lieberman, B.; Ruan, X.; Mathaha, T.; Dahbi, S.-E.; Choma, J.; Kawonga, M.; Mbada, M.; Tripathi, N.; Orbinski, J.; Mellado, B.; Wu, J. Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory. Int. J. Environ. Res. Public Health 2021, 18, 7376. https://doi.org/10.3390/ijerph18147376

AMA Style

Stevenson F, Hayasi K, Bragazzi NL, Kong JD, Asgary A, Lieberman B, Ruan X, Mathaha T, Dahbi S-E, Choma J, Kawonga M, Mbada M, Tripathi N, Orbinski J, Mellado B, Wu J. Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory. International Journal of Environmental Research and Public Health. 2021; 18(14):7376. https://doi.org/10.3390/ijerph18147376

Chicago/Turabian Style

Stevenson, Finn, Kentaro Hayasi, Nicola L. Bragazzi, Jude D. Kong, Ali Asgary, Benjamin Lieberman, Xifeng Ruan, Thuso Mathaha, Salah-Eddine Dahbi, Joshua Choma, Mary Kawonga, Mduduzi Mbada, Nidhi Tripathi, James Orbinski, Bruce Mellado, and Jianhong Wu. 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" International Journal of Environmental Research and Public Health 18, no. 14: 7376. https://doi.org/10.3390/ijerph18147376

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