Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory Article Swipe
Finn Stevenson
,
Kentaro Hayasi
,
Nicola Luigi Bragazzi
,
Jude Dzevela Kong
,
Ali Asgary
,
Benjamin Lieberman
,
X. Ruan
,
Thuso Mathaha
,
Salah-Eddine Dahbi
,
Joshua Choma
,
Mary Kawonga
,
Mduduzi Mbada
,
Nidhi Tripathi
,
James Orbinski
,
B. Mellado
,
Jian Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph18147376
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph18147376
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.
Related Topics
Concepts
Pandemic
Variety (cybernetics)
Scarcity
Psychological intervention
Recurrent neural network
Computer science
Term (time)
Coronavirus disease 2019 (COVID-19)
Data science
Risk analysis (engineering)
Geography
Artificial neural network
Disease
Business
Operations research
Medicine
Artificial intelligence
Economics
Engineering
Infectious disease (medical specialty)
Microeconomics
Quantum mechanics
Pathology
Psychiatry
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph18147376
- https://www.mdpi.com/1660-4601/18/14/7376/pdf?version=1626068256
- OA Status
- gold
- Cited By
- 11
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3164519398
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3164519398Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijerph18147376Digital Object Identifier
- Title
-
Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term MemoryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-09Full publication date if available
- Authors
-
Finn Stevenson, Kentaro Hayasi, Nicola Luigi Bragazzi, Jude Dzevela Kong, Ali Asgary, Benjamin Lieberman, X. Ruan, Thuso Mathaha, Salah-Eddine Dahbi, Joshua Choma, Mary Kawonga, Mduduzi Mbada, Nidhi Tripathi, James Orbinski, B. Mellado, Jian WuList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph18147376Publisher landing page
- PDF URL
-
https://www.mdpi.com/1660-4601/18/14/7376/pdf?version=1626068256Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1660-4601/18/14/7376/pdf?version=1626068256Direct OA link when available
- Concepts
-
Pandemic, Variety (cybernetics), Scarcity, Psychological intervention, Recurrent neural network, Computer science, Term (time), Coronavirus disease 2019 (COVID-19), Data science, Risk analysis (engineering), Geography, Artificial neural network, Disease, Business, Operations research, Medicine, Artificial intelligence, Economics, Engineering, Infectious disease (medical specialty), Microeconomics, Quantum mechanics, Pathology, Psychiatry, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2, 2023: 5, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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