Evidence map›Paper›PMID 27127415›Full record

ArticleBiomedical engineering and computational biology2016

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data.

Anna L Buczak, Benjamin Baugher, Erhan Guven, Linda Moniz, Steven M Babin, Jean-Paul Chretien

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering and computational biology, 2016. 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. Review
  3. Predicting influenza with dynamical methods.BMC medical informatics and decision making · 2016
    Article
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.

Anna L BuczakJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Benjamin BaugherJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Erhan GuvenJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Linda MonizJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Steven M BabinJohns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Jean-Paul ChretienArmed Forces Health Surveillance Branch, Defense Health Agency, Silver Spring, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.

Indexed as

association rule miningfuzzy logicinfluenzapredictionpredictor variables

Identifiers

PMID27127415
PMCPMC4838055

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.