Uso de la herramienta Google Trends para estimar la incidencia de enfermedades tipo influenza en Argentina

Orellano, Pablo Wenceslao; Reynoso, Julieta; Antman, Julián; Argibay, Osvaldo

doi:10.1590/0102-311x00072814

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Multimedia Appendix 1 consists of the first classification of the selected studies [ 27 , 39 - 57 , 59 - 93 , 96 - 144 ]; there are 104 in total, as the studies of Kohler et al [ 145 ], Orellano et al [ 146 ], Cjuno et al [ 147 ], Tejada-Llacsa [ 148 ], and Yang et al [ 149 ] are written in German, Spanish, or Chinese, and thus are not included in the more detailed categorization and analysis.…”

Section: Resultsmentioning

confidence: 99%

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Mavragani¹,

Ochoa²,

Tsagarakis³

2018

J Med Internet Res

249

179

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BackgroundIn the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.ObjectiveThis systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.ResultsAll the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.ConclusionsThe monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

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Section: Resultsmentioning

confidence: 99%

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Mavragani¹,

Ochoa²,

Tsagarakis³

2018

J Med Internet Res

249

179

View full text Add to dashboard Cite

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“…With the exception of a limited study in Argentina [15], there are no published studies evaluating the accuracy of GFT in any of the Latin American countries in which the service was offered. Given that GFT was developed in the United States using North American influenza epidemiology and Internet search activity, its performance in non-US regions may be limited, particularly in tropical regions that can have irregular seasonality [16] and particularly in regions in which English is not the predominant language spoken.…”

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confidence: 99%

Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection

et al. 2016

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“…We did not find a strong correlation between the number of electronically identified ILI and the number of reported, laboratory-confirmed cases of influenza, probably because the 2016 flu season ended around May in Guatemala [ 32 ]; however, we found a strong positive correlation ( r =.8) between the number of electronically identified ILI and the number of reported, laboratory-confirmed cases of noninfluenza respiratory viruses cases from epidemiological week 41 in 2016 to 13 in 2017 ( Table 3 ; Figure 2 ), precisely during a period of increased circulation of noninfluenza respiratory viruses. The study shows the viability of capturing valuable citizen-sourced health data and seems more culturally appropriate than other recently studied alternatives [ 24 , 30 ] in the Latin-American realm. Our choice of reporting platforms, SMS and an Android app, maximizes the chances of having a culturally appropriate solution and minimizes additional software development efforts in a scale-up scenario for Central America.…”

Section: Discussionmentioning

confidence: 89%

“…SMS provides a cross-platform mobile communication channel that is widely used in the country, where mobile penetration rates exceed 100% [ 29 ]. Android smartphones dominate Central and South American markets with approximately 80% of the market share [ 30 ]; in Guatemala, the leading telecom—with 54.5% of active mobile lines—reported that the four most used smartphones in 2016 ran on the Android operating system [ 28 ]. We conducted a study between August 2016 and March 2017 in San Marcos, Guatemala, to assess the relevance, performance, and potential of the ILI-tailored reporting system.…”

Section: Introductionmentioning

confidence: 99%

Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala

Prieto

Jara

Alvis

et al. 2017

JMIR Public Health Surveill

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BackgroundIn many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system.ObjectiveThe objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI.MethodsWe recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system.ResultsNearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017).ConclusionsOur system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people’s fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions.

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Uso de la herramienta Google Trends para estimar la incidencia de enfermedades tipo influenza en Argentina

Cited by 9 publications

References 26 publications

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection

Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala

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