Evidence map›Paper›PMID 39922066›Full record

ArticleEpidemics2025

Enhanced testing can substantially improve defense against several types of respiratory virus pandemic.

James Petrie, James A Hay, Oraya Srimokla, Jasmina Panovska-Griffiths, Charles Whittaker, Joanna Masel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

6 authors.

James PetriePandemic Sciences Institute, University of Oxford, United Kingdom; Big Data Institute, University of Oxford, United Kingdom. Electronic address: jimpetrie@uwaterloo.ca.
James A HayPandemic Sciences Institute, University of Oxford, United Kingdom; Big Data Institute, University of Oxford, United Kingdom.
Oraya SrimoklaNuffield Department of Medicine, University of Oxford, United Kingdom.
Jasmina Panovska-GriffithsPandemic Sciences Institute, University of Oxford, United Kingdom; Big Data Institute, University of Oxford, United Kingdom; UK Health Security Agency, United Kingdom; The Queen's College, University of Oxford, United Kingdom.
Charles WhittakerDepartment of Infectious Disease Epidemiology, Imperial College London, United Kingdom.
Joanna MaselEcology & Evolutionary Biology, University of Arizona, United States of America.

Funding

Wellcome Trust 206298Wellcome Trust 224190Wellcome Trust 225001
6 · The paper itself

Abstract

Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It offers the prospect of averting hospitalizations and deaths whilst avoiding the need for indiscriminate social distancing measures. To understand scenarios where mass testing might or might not be a viable intervention, here we modeled how effectiveness depends both on characteristics of the pathogen (R

Indexed as

Mass ScreeningPandemicsRespiratory Tract InfectionsVirus DiseasesCOVID-19HumansSARS-CoV-2Viral LoadMass testingMathematical modelNon-pharmaceutical interventionPandemic responseTest sensitivity

Identifiers

PMID39922066
PMCPMC7617967

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.