Performance of early warning signals for disease re-emergence: A case study on COVID-19 data Article Swipe
Daniele Proverbio
,
Françoise Kemp
,
Stefano Magni
,
Jorge Gonçalves
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1009958
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1009958
Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.
Related Topics
Concepts
Warning system
Noise (video)
Computer science
Variance (accounting)
Coronavirus disease 2019 (COVID-19)
Set (abstract data type)
Econometrics
Data science
Autocorrelation
Disease
Risk analysis (engineering)
Artificial intelligence
Statistics
Mathematics
Medicine
Infectious disease (medical specialty)
Business
Pathology
Accounting
Image (mathematics)
Telecommunications
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1009958
- OA Status
- gold
- Cited By
- 32
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220706185
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4220706185Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pcbi.1009958Digital Object Identifier
- Title
-
Performance of early warning signals for disease re-emergence: A case study on COVID-19 dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-30Full publication date if available
- Authors
-
Daniele Proverbio, Françoise Kemp, Stefano Magni, Jorge GonçalvesList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pcbi.1009958Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pcbi.1009958Direct OA link when available
- Concepts
-
Warning system, Noise (video), Computer science, Variance (accounting), Coronavirus disease 2019 (COVID-19), Set (abstract data type), Econometrics, Data science, Autocorrelation, Disease, Risk analysis (engineering), Artificial intelligence, Statistics, Mathematics, Medicine, Infectious disease (medical specialty), Business, Pathology, Accounting, Image (mathematics), Telecommunications, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
32Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 13, 2024: 6, 2023: 8, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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66Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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