Literature DB >> 30342281

Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis.

V Gianfredi1, N L Bragazzi2, M Mahamid3, B Bisharat4, N Mahroum5, H Amital5, M Adawi6.   

Abstract

OBJECTIVES: Pertussis is a vaccine-preventable disease. Despite this, it remains a major health problem among children in developing countries and in recent years, has re-emerged and has led to considerable outbreaks. Pertussis surveillance is of paramount importance; however, classical monitoring approaches are plagued by some shortcomings, such as considerable time delay and potential underestimation/underreporting of cases. STUDY
DESIGN: This study aims at investigating the possibility of using Google Trends (GT) as an instrument for tracking pertussis outbreaks to see if infodemiology and infoveillance approaches could overcome the previously mentioned issues because they are based on real-time monitoring and tracking of web-related activities.
METHODS: In the present study, GT was mined from inception (01 January 2004) to 31 December 2015 in the different European countries. Pertussis was searched using the 'search topic' strategy. Pertussis-related GT figures were correlated with the number of pertussis cases and deaths retrieved from the European Centre for Disease prevention and Control database.
RESULTS: At the European countries level, correlation between pertussis cases and GT-based search volumes was very large (ranging from 0.94 to 0.97) from 2004 to 2015. When examining each country, however, only a few reached the threshold of statistical significance.
CONCLUSIONS: GT could be particularly useful in pertussis surveillance and control, provided that the algorithm is better adjusted and refined at the country level.
Copyright © 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Big data; Fast data; Google Tends; Infodemiology; Pertussis

Mesh:

Year:  2018        PMID: 30342281     DOI: 10.1016/j.puhe.2018.09.001

Source DB:  PubMed          Journal:  Public Health        ISSN: 0033-3506            Impact factor:   2.427


  11 in total

1.  Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan.

Authors:  Yu-Wei Chang; Wei-Lun Chiang; Wen-Hung Wang; Chun-Yu Lin; Ling-Chien Hung; Yi-Chang Tsai; Jau-Ling Suen; Yen-Hsu Chen
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Authors:  Joanna Kedra; Timothy Radstake; Aridaman Pandit; Xenofon Baraliakos; Francis Berenbaum; Axel Finckh; Bruno Fautrel; Tanja A Stamm; David Gomez-Cabrero; Christian Pristipino; Remy Choquet; Hervé Servy; Simon Stones; Gerd Burmester; Laure Gossec
Journal:  RMD Open       Date:  2019-07-18

3.  Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.

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4.  Temporary Fertility Decline after Large Rubella Outbreak, Japan.

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Journal:  Emerg Infect Dis       Date:  2020-06       Impact factor: 6.883

5.  Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study.

Authors:  Muhammad Syamsuddin; Muhammad Fakhruddin; Jane Theresa Marlen Sahetapy-Engel; Edy Soewono
Journal:  J Med Internet Res       Date:  2020-07-24       Impact factor: 5.428

6.  Burden of measles using disability-adjusted life years, Umbria 2013-2018.

Authors:  Vincenza Gianfredi; Massimo Moretti; Igino Fusco Moffa
Journal:  Acta Biomed       Date:  2020-04-10

7.  Vaccines are underused in pregnancy: what about knowledge, attitudes and practices of providers?

Authors:  Matteo Riccò; Luigi Vezzosi; Federica Balzarini; Giovanni Gualerzi; Silvia Ranzieri; Rola Khamisy-Farah; Nicola Luigi Bragazzi
Journal:  Acta Biomed       Date:  2020-04-10

8.  Correlation between flu and Wikipedia's pages visualization.

Authors:  Vincenza Gianfredi; Omar Enzo Santangelo; Sandro Provenzano
Journal:  Acta Biomed       Date:  2021-02-08

9.  Forecasting the future number of pertussis cases using data from Google Trends.

Authors:  Dominik Nann; Mark Walker; Leonie Frauenfeld; Tamás Ferenci; Mihály Sulyok
Journal:  Heliyon       Date:  2021-11-12

10.  Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study.

Authors:  Jiawei Li; Qing Xu; Raphael Cuomo; Vidya Purushothaman; Tim Mackey
Journal:  JMIR Public Health Surveill       Date:  2020-04-21
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