Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1098/rsif.2022.0123
Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1098/rsif.2022.0123
- OA Status
- hybrid
- Cited By
- 14
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289526490
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289526490Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1098/rsif.2022.0123Digital Object Identifier
- Title
-
Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based modelsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-01Full publication date if available
- Authors
-
Andrew T. Tredennick, Eamon B. O’Dea, Matthew J. Ferrari, Andrew Park, Pejman Rohani, John M. DrakeList of authors in order
- Landing page
-
https://doi.org/10.1098/rsif.2022.0123Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1098/rsif.2022.0123Direct OA link when available
- Concepts
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Warning system, Computer science, Measles, Judgement, Outbreak, Infectious disease (medical specialty), Risk analysis (engineering), Disease, Early warning system, Data science, Econometrics, Operations research, Biology, Medicine, Economics, Virology, Mathematics, Telecommunications, Political science, Law, Pathology, VaccinationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
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2025: 1, 2024: 7, 2023: 3, 2022: 3Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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