ArticleJournal of the Royal Society, Interface2023
Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology.
Article in Journal of the Royal Society, Interface, 2023. 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.
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Adaptive multi-model ensembles for improved epidemic projections and decision support.medRxiv : the preprint server for health sciences · 2026Article
- A Software Platform for Collaborative Infectious Disease Modeling.Nature health · 2026Article
- Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.medRxiv : the preprint server for health sciences · 2026Article
- Multi-Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R.Statistics in medicine · 2026Article
- What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge.Ecological applications : a publication of the Ecological Society of America · 2025Article
- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.Epidemics · 2024Article
- Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.PLoS medicine · 2024Article
- The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy.Epidemics · 2024Article
- Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.Nature communications · 2023Article
- Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.medRxiv : the preprint server for health sciences · 2023Article
- Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers.GeoHealth · 2023Article
- Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design.medRxiv : the preprint server for health sciences · 2023Article
- Multiple models for outbreak decision support in the face of uncertainty.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology.Journal of the Royal Society, Interface · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
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.