ArticleInterface focus2021
Disease transmission and control modelling at the science-policy interface.
Article in Interface focus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Formal and informal science-policy interaction during public health emergencies of international concern: a scoping review.BMC global and public health · 2026Pooled it
- Mapping risk communication practices in public health emergencies: a scoping review and comparison with Italian regional pandemic plans.BMC public health · 2026Article
- Enhancing epidemic forecast usability for policymakers: A global mixed-methods study.PLOS global public health · 2026Article
- Data generation and modeling during COVID-19: utility, barriers, and priorities for future investments in public health response.Frontiers in public health · 2026Article
- Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.American journal of epidemiology · 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
- The future of pandemic modeling in support of decision making: lessons learned from COVID-19.BMC global and public health · 2025Article
- The utility of infectious disease modelling in informing decisions for outbreak response: A scoping review.PLOS global public health · 2025Article
- The decision uncertainty toolkit: Risk measures and visual outputs to support decision making during public health crises.PloS one · 2025Article
- Policy impact of the Imperial College COVID-19 Response Team: global perspective and United Kingdom case study.Health research policy and systems · 2024Article
- Inference on spatiotemporal dynamics for coupled biological populations.Journal of the Royal Society, Interface · 2024Article
- Informing policy via dynamic models: Cholera in Haiti.PLoS computational biology · 2024Article
- Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic.Wellcome open research · 2024Article
- Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom.Journal of the Royal Society, Interface · 2023Article
- Article
- A consensus of evidence: The role of SPI-M-O in the UK COVID-19 response.Advances in biological regulation · 2022Article
- An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation.The Lancet. Digital health · 2022Review
- Disease transmission and control modelling at the science-policy interface.Interface focus · 2021Article
- Communicating uncertainty in epidemic models.Epidemics · 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
2 authors.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has disrupted the lives of billions across the world. Mathematical modelling has been a key tool deployed throughout the pandemic to explore the potential public health impact of an unmitigated epidemic. The results of such studies have informed governments' decisions to implement non-pharmaceutical interventions to control the spread of the virus. In this article, we explore the complex relationships between models, decision-making, the media and the public during the COVID-19 pandemic in the United Kingdom of Great Britain and Northern Ireland (UK). Doing so not only provides an important historical context of COVID-19 modelling and how it has shaped the UK response, but as the pandemic continues and looking towards future pandemic preparedness, understanding these relationships and how they might be improved is critical. As such, we have synthesized information gathered via three methods: a survey to publicly list attendees of the Scientific Advisory Group for Emergencies, the Scientific Pandemic Influenza Group on Modelling and other comparable advisory bodies, interviews with science communication experts and former scientific advisors, and reviewing some of the key COVID-19 modelling literature from 2020. Our research highlights the desire for increased bidirectional communication between modellers, decision-makers and the public, as well as the need to convey uncertainty inherent in transmission models in a clear manner. These aspects should be considered carefully ahead of the next emergency response.
Indexed as
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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.