ReviewPatterns (New York, N.Y.)2021
Accounting for uncertainty during a pandemic.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Mobility-informed metapopulation models predict the spatio-temporal spread of respiratory epidemics across scales.BMC global and public health · 2026Article
- Unjustified Poisson assumptions lead to overconfident estimates of the effective reproductive number.Epidemiology and infection · 2026Article
- An analysis of spatial and temporal uncertainty propagation in agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Open-sourced modeling and simulating tools for decision-makers during an emerging pandemic or epidemic - Systematic evaluation of utility and usability: A scoping review update.Dialogues in health · 2024Article
- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates.PLoS computational biology · 2024Article
- Neural parameter calibration and uncertainty quantification for epidemic forecasting.PloS one · 2024Article
- Enriching single-arm clinical trials with external controls: possibilities and pitfalls.Blood advances · 2023Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic.The Lancet. Public health · 2023Review
- Epidemic models: why and how to use them.Anaesthesia, critical care & pain medicine · 2022Article
- Communicating uncertainty in epidemic models.Epidemics · 2021Article
- Nowcasting towards sustainable SARS-CoV-2 endemicity.Lancet (London, England) · 2021Article
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
We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.
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.