ArticlePLoS computational biology2024
Best practices for estimating and reporting epidemiological delay distributions of infectious diseases.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Estimates of epidemiological parameters for H5N1 influenza in humans: a rapid review.BMC infectious diseases · 2025Pooled it
- A generalized multi-population model for Ebola virus transmission incorporating environmental reservoir dynamics.Infectious medicine · 2026Article
- A systematic comparison of methodologies for the estimation of the serial interval.Infectious Disease Modelling · 2026Article
- Quantifying, Understanding, and Correcting for Delays in Routine Dengue Case Reporting in the Americas.The American journal of tropical medicine and hygiene · 2026Article
- Public reporting guidelines for outbreak data: Enabling accountability for effective outbreak response by developing standards for transparency and uniformity.Public health · 2026Article
- Article
- Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023/24.Epidemiology and infection · 2025Article
- Multi-model approach to understand and predict past and future dengue epidemic dynamics.Royal Society open science · 2025Article
- A Bayesian model for repeated cross-sectional epidemic prevalence survey data.PLoS computational biology · 2025Article
- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- Nonparametric serial interval estimation with uniform mixtures.PLoS computational biology · 2025Article
- The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam.BMC infectious diseases · 2025Article
- Identification and attribution of weekly periodic biases in global epidemiological time series data.BMC research notes · 2025Article
- Estimation of end-of-outbreak probabilities in the presence of delayed and incomplete case reporting.Proceedings. Biological sciences · 2025Article
- Best practices for estimating and reporting epidemiological delay distributions of infectious diseases.PLoS computational biology · 2024Article
- rtestim: Time-varying reproduction number estimation with trend filtering.PLoS computational biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.
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.