Evidence map›Paper›PMID 28797710›Full record

ArticleJournal of biomedical informatics2017

A Bayesian system to detect and characterize overlapping outbreaks.

John M Aronis, Nicholas E Millett, Michael M Wagner, Fuchiang Tsui, Ye Ye, Jeffrey P Ferraro, Peter J Haug, Per H Gesteland, Gregory F Cooper

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

John M AronisReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: jma18@pitt.edu.
Nicholas E MillettReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Michael M WagnerReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Fuchiang TsuiReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Ye YeReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Jeffrey P FerraroDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Intermountain Healthcare, Salt Lake City, UT, USA.
Peter J HaugDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Intermountain Healthcare, Salt Lake City, UT, USA.
Per H GestelandDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Intermountain Healthcare, Salt Lake City, UT, USA; Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
Gregory F CooperReal-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.

Funding

The internship in Biomedical Research, Informatics, and Computer Science (iBRIC): Biomedical Informatics and Data Science research experiences for students from Minority Serving InstitutionsT15LM007059 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRY S HOCHHEISER · 1987 to 2026
$23.9M
MIDAS Informatics Services Group (ISG)U24GM110707 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ESPINO, JESSI U, WAGNER, MICHAEL MATTHEW · 2014 to 2018
$11.6M
Probabilistic Disease SurveillanceR01LM011370 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WAGNER, MICHAEL MATTHEW · 2013 to 2015
$1.7M
NIGMS NIH HHS U24 GM110707NLM NIH HHS R01 LM011370NLM NIH HHS T15 LM007059
6 · The paper itself

Abstract

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising.

Indexed as

Bayes TheoremDisease OutbreaksCommunicable DiseasesHumansInfluenza, HumanProbabilityBayesian modelingInfluenzaOutbreak characterizationOutbreak detection

Identifiers

PMID28797710
PMCPMC5604259

What OpenQuestion holds

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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.