Evidence map›Paper›PMID 28207777›Full record

ArticlePLoS computational biology2017

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns.

Naomi V Bradbury, William J M Probert, Katriona Shea, Michael C Runge, Christopher J Fonnesbeck, Matt J Keeling, Matthew J Ferrari, Michael J Tildesley

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.2field-weighted citation impact, top 9% of its field
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

12 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
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  7. Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
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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

8 authors at 5 institutions in 2 countries.

Naomi V BradburySchool of Veterinary Medicine and Science, University of Nottingham, Leicestershire, United Kingdom.ORCID 0000-0002-8934-6301
William J M ProbertSchool of Veterinary Medicine and Science, University of Nottingham, Leicestershire, United Kingdom.
Katriona SheaCenter for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Michael C RungeUS Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland, United States of America.ORCID 0000-0002-8081-536X
Christopher J FonnesbeckDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
Matt J KeelingSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.ORCID 0000-0003-4639-4765
Matthew J FerrariCenter for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Michael J TildesleySchool of Veterinary Medicine and Science, University of Nottingham, Leicestershire, United Kingdom.
University of Nottingham · GBPennsylvania State University · USUnited States Geological Survey · USUniversity of Warwick · GBVanderbilt University · US

Funding

Linking models and policy: Using active adaptive management for optimal control of disease outbreaksR01GM105247 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI FERRARI, MATTHEW · 2012 to 2016
$1.3M
Biotechnology and Biological Sciences Research Council BB/K010972/4NIGMS NIH HHS R01 GM105247
6 · The paper itself

Abstract

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning.

Indexed as

Animal CullingAnimalsCost-Benefit AnalysisEmergency Medical ServicesFoot-and-Mouth DiseaseHealth Care CostsImmunization ProgramsMass VaccinationPopulation SurveillancePrevalenceRisk AssessmentUnited KingdomViral VaccinesViral Vaccines

Identifiers

PMID28207777
PMCPMC5312803
OpenAlexW2587189779

What OpenQuestion holds

Textmetadata
LicenceCC0
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.