ArticlePLoS computational biology2018
Anticipating epidemic transitions with imperfect data.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Multivariate resilience indicators to anticipate vector-borne disease outbreaks: A West Nile virus case-study.PLoS computational biology · 2025Article
- Identifying COVID-19 peaks using early warning signals.PLoS computational biology · 2025Article
- Measuring Health System Resilience During the COVID-19 Pandemic Using Dynamic Indicators of Resilience Based on Sick-Leave Data.Health science reports · 2025Article
- Early warning signals do not predict a warming-induced experimental epidemic.PLOS global public health · 2025Article
- How mathematical modelling can inform outbreak response vaccination.BMC infectious diseases · 2024Review
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.Frontiers in network physiology · 2024Article
- The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.PLOS global public health · 2023Article
- A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden.The Annals of regional science · 2022Article
- Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.Journal of the Royal Society, Interface · 2022Article
- Balancing Quarantine and Self-Distancing Measures in Adaptive Epidemic Networks.Bulletin of mathematical biology · 2022Article
- Performance of early warning signals for disease re-emergence: A case study on COVID-19 data.PLoS computational biology · 2022Article
- Overlapping timescales obscure early warning signals of the second COVID-19 wave.Proceedings. Biological sciences · 2022Article
- Early warning signal reliability varies with COVID-19 waves.Biology letters · 2021Article
- Early warning signals of infectious disease transitions: a review.Journal of the Royal Society, Interface · 2021Review
- Prospects for detecting early warning signals in discrete event sequence data: Application to epidemiological incidence data.PLoS computational biology · 2020Article
- Dynamical footprints enable detection of disease emergence.PLoS biology · 2020Article
- Detecting critical slowing down in high-dimensional epidemiological systems.PLoS computational biology · 2020Article
- Early warning signals of malaria resurgence in Kericho, Kenya.Biology letters · 2020Article
- The problem of detrending when analysing potential indicators of disease elimination.Journal of theoretical biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, which are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases.
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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.