Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives Article Swipe
Hua‐Liang Wei
,
S.A. Billings
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01763
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01763
Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables pre-selected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective. The present study, for the first time, proposes using systems engineering and system identification approach to build transparent, interpretable, parsimonious and simulatable (TIPS) dynamic machine learning models, establishing links between the R number, the infection cases and deaths caused by COVID-19. The TIPS models are developed based on the well-known NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) model, which can help better understand the COVID-19 pandemic dynamics. A case study on the UK COVID-19 data is carried out, and new findings are detailed. The proposed method and the associated new findings are useful for better understanding the spread dynamics of the COVID-19 pandemic.
Related Topics
Concepts
Interpretability
Identification (biology)
Computer science
Coronavirus disease 2019 (COVID-19)
Ode
Ordinary differential equation
Pandemic
A priori and a posteriori
Artificial intelligence
Autoregressive model
System dynamics
Machine learning
Econometrics
Mathematics
Differential equation
Applied mathematics
Biology
Infectious disease (medical specialty)
Botany
Disease
Epistemology
Pathology
Medicine
Philosophy
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.01763
- https://arxiv.org/pdf/2111.01763
- OA Status
- green
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3208728071
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3208728071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.01763Digital Object Identifier
- Title
-
Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification PerspectivesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-01Full publication date if available
- Authors
-
Hua‐Liang Wei, S.A. BillingsList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.01763Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.01763Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.01763Direct OA link when available
- Concepts
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Interpretability, Identification (biology), Computer science, Coronavirus disease 2019 (COVID-19), Ode, Ordinary differential equation, Pandemic, A priori and a posteriori, Artificial intelligence, Autoregressive model, System dynamics, Machine learning, Econometrics, Mathematics, Differential equation, Applied mathematics, Biology, Infectious disease (medical specialty), Botany, Disease, Epistemology, Pathology, Medicine, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2021-11-01 |
| publication_year | 2021 |
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| abstract_inverted_index.R | 200 |
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| abstract_inverted_index.and | 27, 39, 47, 71, 122, 156, 166, 180, 189, 205, 248, 256 |
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| abstract_inverted_index.TIPS | 211 |
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| abstract_inverted_index.case | 238 |
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| abstract_inverted_index.(TIPS) | 191 |
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| abstract_inverted_index.NARMAX | 219 |
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| abstract_inverted_index.useful | 262 |
| abstract_inverted_index.(ODEs). | 65 |
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