Complexity of COVID-19 Dynamics Article Swipe
Bellie Sivakumar
,
B. Deepthi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/e24010050
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/e24010050
With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.
Related Topics
Concepts
Curse of dimensionality
Coronavirus disease 2019 (COVID-19)
Pandemic
Population
Dimension (graph theory)
Dynamics (music)
Time series
Outbreak
Computer science
Mathematics
Econometrics
Statistics
Demography
Medicine
Infectious disease (medical specialty)
Virology
Physics
Sociology
Pathology
Disease
Pure mathematics
Acoustics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e24010050
- https://www.mdpi.com/1099-4300/24/1/50/pdf?version=1640670522
- OA Status
- gold
- Cited By
- 17
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200590485
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4200590485Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/e24010050Digital Object Identifier
- Title
-
Complexity of COVID-19 DynamicsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-27Full publication date if available
- Authors
-
Bellie Sivakumar, B. DeepthiList of authors in order
- Landing page
-
https://doi.org/10.3390/e24010050Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/24/1/50/pdf?version=1640670522Direct 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/1099-4300/24/1/50/pdf?version=1640670522Direct OA link when available
- Concepts
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Curse of dimensionality, Coronavirus disease 2019 (COVID-19), Pandemic, Population, Dimension (graph theory), Dynamics (music), Time series, Outbreak, Computer science, Mathematics, Econometrics, Statistics, Demography, Medicine, Infectious disease (medical specialty), Virology, Physics, Sociology, Pathology, Disease, Pure mathematics, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 9, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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71Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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