ArticleTheoretical biology & medical modelling2017
Forecasting infectious disease emergence subject to seasonal forcing.
Article in Theoretical biology & medical modelling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
25 citing papers in PubMed.
- Spatial Individual-Level Models for Transmission Dynamics of Seasonal Infectious Diseases.Statistics in medicine · 2026Article
- ModelingInfectious Disease Modelling · 2025Article
- 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
- Early warning signals do not predict a warming-induced experimental epidemic.PLOS global public health · 2025Article
- Using neural ordinary differential equations to predict complex ecological dynamics from population density data.Journal of the Royal Society, Interface · 2024Article
- Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion.One health (Amsterdam, Netherlands) · 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
- 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
- Transient indicators of tipping points in infectious diseases.Journal of the Royal Society, Interface · 2020Article
- 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
- Early warning signals of malaria resurgence in Kericho, Kenya.Biology letters · 2020Article
- Detecting critical slowing down in high-dimensional epidemiological systems.PLoS computational biology · 2020Article
- The problem of detrending when analysing potential indicators of disease elimination.Journal of theoretical biology · 2019Article
- The statistics of epidemic transitions.PLoS computational 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
4 authors.
Funding
Abstract
backgroundDespite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models.
methodsWe simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic.
resultsEarly warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly.
conclusionsEarly warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.
Indexed as
Identifiers
What OpenQuestion holds
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.