Evidence map›Paper›PMID 26543674›Full record

ArticlePLoS currents2015

Validating the Use of Google Trends to Enhance Pertussis Surveillance in California.

Simon Pollett, Nicholas Wood, W John Boscardin, Henrik Bengtsson, Sandra Schwarcz, Kathleen Harriman, Kathleen Winter, George Rutherford

Abstract read
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In one paragraph

Article in PLoS currents, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  12. Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2017
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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.

Simon PollettMarie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, NSW, Australia; Department of Epidemiology & Biostatistics, University of California at San Francisco, California, USA.
Nicholas WoodNational Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases, The Children's Hospital at Westmead, Sydney, NSW, Australia; Discipline of Paediatrics and Child Health, University of Sydney, NSW, Australia.
W John BoscardinDepartment of Epidemiology & Biostatistics, University of California at San Francisco, California, USA; Department of Medicine, University of California at San Francisco, California, USA.
Henrik BengtssonDepartment of Epidemiology & Biostatistics, University of California at San Francisco, California, USA.
Sandra SchwarczDepartment of Epidemiology & Biostatistics, University of California at San Francisco, California, USA.
Kathleen HarrimanCalifornia Department of Public Health, Richmond, California, USA.
Kathleen WinterCalifornia Department of Public Health, Richmond, California, USA.
George RutherfordDepartment of Epidemiology & Biostatistics, University of California at San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

methodsPertussis has recently re-emerged in the United States. Timely surveillance is vital to estimate the burden of this disease accurately and to guide public health response. However, the surveillance of pertussis is limited by delays in reporting, consolidation and dissemination of data to relevant stakeholders. We fit and assessed a real-time predictive Google model for pertussis in California using weekly incidence data from 2009-2014. RESULTS AND DISCUSSION: The linear model was moderately accurate (r = 0.88). Our findings cautiously offer a complementary, real-time signal to enhance pertussis surveillance in California and help to further define the limitations and potential of Google-based epidemic prediction in the rapidly evolving field of digital disease detection.

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