Evidence map›Paper›PMID 32546159›Full record

ArticleBMC public health2020

Investigating the utility of Google trends for Zika and Chikungunya surveillance in Venezuela.

Ricardo Strauss, Eva Lorenz, Kaja Kristensen, Daniel Eibach, Jaime Torres, Jürgen May, Julio Castro

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022
    Review
  4. Decreased Global Interest in Oral Cancer During the COVID-19 Pandemic.Asian Pacific journal of cancer prevention : APJCP · 2021
    Article
  5. Article
  6. Article
  7. 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

7 authors.

Ricardo StraussBernhard Nocht Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany. ricardo.strauss@bnitm.de.
Eva LorenzBernhard Nocht Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany.
Kaja KristensenBernhard Nocht Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany.
Daniel EibachBernhard Nocht Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany.
Jaime TorresInstituto de Medicina Tropical, Universidad Central de Venezuela, Caracas, Venezuela.
Jürgen MayBernhard Nocht Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany.
Julio CastroInstituto de Medicina Tropical, Universidad Central de Venezuela, Caracas, Venezuela.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChikungunya and Zika Virus are vector-borne diseases responsible for a substantial disease burden in the Americas. Between 2013 and 2016, no cases of Chikungunya or Zika Virus were reported by the Venezuelan Ministry of Health. However, peaks of undiagnosed fever cases have been observed during the same period. In the context of scarce data, alternative surveillance methods are needed. Assuming that unusual peaks of acute fever cases correspond to the incidences of both diseases, this study aims to evaluate the use of Google Trends as an indicator of the epidemic behavior of Chikungunya and Zika.

methodsTime-series cross-correlations of acute fever cases reported by the Venezuelan Ministry of Health and data on Google search queries related to Chikungunya and Zika were calculated.

resultsA temporal distinction has been made so that acute febrile cases occurring between 25th of June 2014 and 23rd of April 2015 were attributed to the Chikungunya virus, while cases occurring between 30th of April 2015 and 29th of April 2016 were ascribed to the Zika virus. The highest cross-correlations for each disease were shown at a lag of 0 (r = 0.784) for Chikungunya and at + 1 (r = 0.754) for Zika.

conclusionThe strong positive correlation between Google search queries and official data on acute febrile cases suggests that this resource can be used as an indicator of endemic urban arboviruses activity. In the Venezuelan context, Internet search queries might help to overcome some of the gaps that exist in the national surveillance system.

Indexed as

ArbovirusesInformation Seeking BehaviorInternetChikungunya FeverChikungunya virusDengueDengue VirusEpidemicsFeverGovernment AgenciesHumansIncidencePopulation SurveillanceSearch EngineUrban PopulationVenezuelaChikungunyaInternet search queriesSurveillance systemsZika

Identifiers

PMID32546159
PMCPMC7298838

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.