ArticleEpidemics2021
Communicating uncertainty in epidemic models.
Article in Epidemics, 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.
- Visualizing epidemiological models for policy: design principles for effective communication.Frontiers in public health · 2026Article
- A Flurry of Infectious Disease Modeling Tools During the COVID-19 Pandemic, Considerations for Future Selection.Pathogens (Basel, Switzerland) · 2025Review
- The need for methodological pluralism in epidemiological modelling.Global epidemiology · 2025Article
- An analysis of spatial and temporal uncertainty propagation in agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- The future of pandemic modeling in support of decision making: lessons learned from COVID-19.BMC global and public health · 2025Article
- The decision uncertainty toolkit: Risk measures and visual outputs to support decision making during public health crises.PloS one · 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
- Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Neural parameter calibration and uncertainty quantification for epidemic forecasting.PloS one · 2024Article
- Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease.Vaccines · 2023Article
- Article
- Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.Epidemics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.
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.