Evidence map›Paper›PMID 35183834›Full record

ArticleEpidemics2022

Challenges for modelling interventions for future pandemics.

Mirjam E Kretzschmar, Ben Ashby, Elizabeth Fearon, Christopher E Overton, Jasmina Panovska-Griffiths, Lorenzo Pellis, Matthew Quaife, Ganna Rozhnova, Francesca Scarabel, Helena B Stage and 4 more

Abstract read
In one paragraph

Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Observational
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  13. Assessing the Impact of Haulage Drivers in Uganda's COVID-19 Delta Wave.Journal of epidemiology and global health · 2025
    Article
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  16. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Mirjam E KretzschmarJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. Electronic address: m.e.e.kretzschmar@umcutrecht.nl.
Ben AshbyDepartment of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK.
Elizabeth FearonDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK.
Christopher E OvertonDepartment of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, UK.
Jasmina Panovska-GriffithsThe Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK.
Lorenzo PellisDepartment of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK.
Matthew QuaifeTB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK.
Ganna RozhnovaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
Francesca ScarabelDepartment of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy.
Helena B StageDepartment of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; University of Potsdam, Germany; Humboldt University of Berlin, Germany.
Ben SwallowSchool of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish Covid-19 Response Consortium, UK.
Robin N ThompsonJoint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK.
Michael J TildesleyJoint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK.
Daniel VillelaProgram of Scientific Computing, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.

Funding

Medical Research Council MR/S020462/1Medical Research Council MR/V028618/1Medical Research Council MR/V038613/1
6 · The paper itself

Abstract

Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.

Indexed as

COVID-19PandemicsHumansSARS-CoV-2Mathematical modelsNon-pharmaceutical interventionsPandemicsPharmaceutical interventionsPolicy support

Identifiers

PMID35183834
PMCPMC8830929

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.