ReviewThe Lancet. Digital health2022
An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation.
Review in The Lancet. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Simulating nationwide coupled disease and fear spread in an agent-based model.Scientific reports · 2025Article
- The tale of two assumptions: incorporating healthcare-seeking behaviour in epidemic forecasting.BMC infectious diseases · 2025Article
- Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers' perspective, 2020 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Machine Learning-Driven COVID-19 Hospitalization Forecasting: From Theory to Practice in a Major Northeastern Academic Medical Center.Open forum infectious diseases · 2025Article
- An efficient parallel runoff forecasting model for capturing global and local feature information.Scientific reports · 2025Article
- The future of pandemic modeling in support of decision making: lessons learned from COVID-19.BMC global and public health · 2025Article
- Uncertainty quantification of antibody measurements: Physical principles and implications for standardization.Physical review. E · 2025Article
- Efficiency evaluation on case finding strategy for COVID-19 outbreak control under China's "dynamic zero-case policy": a retrospective field epidemiology study.Frontiers in public health · 2025Observational
- What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?PloS one · 2025Article
- Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling.International journal of environmental research and public health · 2024Article
- When are predictions useful? A new method for evaluating epidemic forecasts.BMC global and public health · 2024Article
- Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback.JMIR human factors · 2024Article
- Modeling zoonotic and vector-borne viruses.Current opinion in virology · 2024Review
- Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review.Dialogues in health · 2024Article
- Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections.Epidemics · 2024Article
- Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map.Journal of public health (Oxford, England) · 2024Review
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.Scientific reports · 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
8 authors.
Funding
Abstract
Infectious disease modelling can serve as a powerful tool for situational awareness and decision support for policy makers. However, COVID-19 modelling efforts faced many challenges, from poor data quality to changing policy and human behaviour. To extract practical insight from the large body of COVID-19 modelling literature available, we provide a narrative review with a systematic approach that quantitatively assessed prospective, data-driven modelling studies of COVID-19 in the USA. We analysed 136 papers, and focused on the aspects of models that are essential for decision makers. We have documented the forecasting window, methodology, prediction target, datasets used, and geographical resolution for each study. We also found that a large fraction of papers did not evaluate performance (25%), express uncertainty (50%), or state limitations (36%). To remedy some of these identified gaps, we recommend the adoption of the EPIFORGE 2020 model reporting guidelines and creating an information-sharing system that is suitable for fast-paced infectious disease outbreak science.
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.