ArticleJournal of the Royal Society, Interface2022
Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reviewing the progress of infectious disease early warning systems and planning for the future.BMC public health · 2024Pooled it
- Article
- Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.PLoS computational biology · 2025Article
- Development of environmental loop-mediated isothermal amplification (eLAMP) diagnostic tool for Bulinus truncatus field detection.Parasites & vectors · 2023Article
- The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.PLOS global public health · 2023Article
- Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.Journal of the Royal Society, Interface · 2022Article
- Overlapping timescales obscure early warning signals of the second COVID-19 wave.Proceedings. Biological sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.