ArticleTheoretical ecology2019
Disentangling reporting and disease transmission.
Article in Theoretical ecology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Estimating dengue force of infection from age-stratified surveillance data in Java, Indonesia.Journal of the Royal Society, Interface · 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
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- A novel correction method for modelling parameter-driven autocorrelated time series with count outcome.BMC public health · 2024Article
- The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.PLOS global public health · 2023Article
- 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
- Identifying critical transitions in complex diseases.Journal of biosciences · 2022Review
- 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
- Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.Frontiers in public health · 2020Article
- 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
2 authors.
Funding
Abstract
Second-order statistics such as the variance and autocorrelation can be useful indicators of the stability of randomly perturbed systems, in some cases providing early warning of an impending, dramatic change in the system's dynamics. One specific application area of interest is the surveillance of infectious diseases. In the context of disease (re-)emergence, a goal could be to have an indicator that is informative of whether the system is approaching the epidemic threshold, a point beyond which a major outbreak becomes possible. Prior work in this area has provided some proof of this principle but has not analytically treated the effect of imperfect observation on the behavior of indicators. This work provides expected values for several moments of the number of reported cases, where reported cases follow a binomial or negative binomial distribution with a mean based on the number of deaths in a birth-death-immigration process over some reporting interval. The normalized second factorial moment and the decay time of the number of reported cases are two indicators that are insensitive to the reporting probability. Simulation is used to show how this insensitivity could be used to distinguish a trend of increased reporting from a trend of increased transmission. The simulation study also illustrates both the high variance of estimates and the possibility of reducing the variance by averaging over an ensemble of estimates from multiple time series.
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.