Evidence map›Paper›PMID 29563241›Full record

ArticleJournal of the Royal Society, Interface2018

Measles outbreak response decision-making under uncertainty: a retrospective analysis.

Christopher J Fonnesbeck, Katriona Shea, Spencer Carran, Jose Cassio de Moraes, Christopher Gregory, James L Goodson, Matthew J Ferrari

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Anti-vaccination movements in the world and in Brazil.Revista da Sociedade Brasileira de Medicina Tropical · 2022
    Article
  2. Article
  3. Review
  4. Article
  5. 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

7 authors.

Christopher J FonnesbeckDepartment of Biostatistics, Vanderbilt University Medical Center, Eleventh Floor, Suite 11000, 2525 West End Avenue, Nashville, TN, USA fonnesbeck@gmail.com.ORCID 0000-0002-7107-140X
Katriona SheaDepartment of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA, USA.
Spencer CarranCenter for Infectious Disease Dynamics, Department of Biology, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA.
Jose Cassio de MoraesFaculdade de Ciencias Medicas da Sanda Casa de São Paulo, São Paulo, Brazil.
Christopher GregoryArboviral Diseases Branch, Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Fort Collins, CO, USA.
James L GoodsonAccelerated Disease Control and Vaccine Preventable Disease Surveillance Branch, Global Immunization Division, Center for Global Health, US Centers for Disease Control and Prevention, Atlanta, GA, USA.
Matthew J FerrariDepartment of Biology and Intercollege Graduate Degree Program in Ecology, 208 Mueller Laboratory, The Pennsylvania State University, University Park, PA, USA.

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
NIGMS NIH HHS R01 GM105247
6 · The paper itself

Abstract

Resurgent outbreaks of vaccine-preventable diseases that have previously been controlled or eliminated have been observed in many settings. Reactive vaccination campaigns may successfully control outbreaks but must necessarily be implemented in the face of considerable uncertainty. Real-time surveillance may provide critical information about at-risk population and optimal vaccination targets, but may itself be limited by the specificity of disease confirmation. We propose an integrated modelling approach that synthesizes historical demographic and vaccination data with real-time outbreak surveillance via a dynamic transmission model and an age-specific disease confirmation model. We apply this framework to data from the 1996-1997 measles outbreak in São Paulo, Brazil. To simulate the information available to decision-makers, we truncated the surveillance data to what would have been available at 1 or 2 months prior to the realized interventions. We use the model, fitted to real-time observations, to evaluate the likelihood that candidate age-targeted interventions could control the outbreak. Using only data available prior to the interventions, we estimate that a significant excess of susceptible adults would prevent child-targeted campaigns from controlling the outbreak and that failing to account for age-specific confirmation rates would underestimate the importance of adult-targeted vaccination.

Indexed as

Clinical Decision-MakingDisease OutbreaksMeaslesModels, BiologicalUncertaintyVaccinationAdolescentAdultBrazilChildChild, PreschoolFemaleHumansInfantMaleMeasles VaccineMeasles VaccineBayesian disease modeldecision analysismeasles epidemicoutbreak controlvaccine intervention

Identifiers

PMID29563241
PMCPMC5908520

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.