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Rovetta A. Google trends in infodemiology: Methodological steps to avoid irreproducible results and invalid conclusions. Int J Med Inform 2024; 190:105563. [PMID: 39043059 DOI: 10.1016/j.ijmedinf.2024.105563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 07/10/2024] [Accepted: 07/20/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND Google Trends is a widely used tool for infodemiological surveys. However, irregularities in the random sampling and aggregation algorithms compromise the reliability of the relative search volume (RSV) and the regional online interest (ROI). OBJECTIVE The study aims to unmask methodological criticalities commonly ignored in carrying out infodemiological surveys via Google Trends. A guide to avoiding these shortcomings is also provided. MATERIAL AND METHODS The Google Topic "Coronavirus disease 2019" has been investigated using different timelapses, categories, and IP addresses. The same samples were manually collected multiple times to evaluate the RSV and ROI stability. Stability was estimated through indicators of variability (e.g., coefficient of percentage variation "CV%" and its 4-surprisal interval "4-I"). The content aggregation capacity of the algorithms relating to topics and categories was evaluated through the quantitative analysis of RSV and ROI and the qualitative examination of the related queries. RESULTS The stability of Google Trends' RSV and ROI is not linked exclusively to the dataset dimension or the IP address. Subregional datasets can be highly unstable (e.g., CV% = 10, 4-I: [8,13]). Google Trends categories and topics can exclude relevant queries or include unnecessary queries. The statistical scenario is consistent with the following hypotheses: i) datasets containing too few queries are highly unstable, ii) the "interest over time" data format is generally reliable for evaluating trends and correlations, iii) Google Trends improvements have altered the RSV historical trends. CONCLUSIONS Google Trends can be an effective and efficient infodemiological tool as long as the reliability of web search indexes is appropriately analyzed and weighted for the scientific goal. The methodological steps discussed in this study are critical to drawing valid and relevant scientific conclusions.
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Xu Y, Margolin D. Collective Information Seeking During a Health Crisis : Predictors of Google Trends During COVID-19. HEALTH COMMUNICATION 2024; 39:388-402. [PMID: 36683356 DOI: 10.1080/10410236.2023.2167578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
This article approaches collective health information seeking from computational method by investigating patterns of Google Trends data in the United States during the early stages of the COVID-19 pandemic. We analyzed factors that prompted a community's curiosity, and information that communities were most curious about. The results of our cross-sectional and time-series-based analyses reveal a few salient findings: (1) Republican leaning states searched less frequently, and while states with more cases searched more, partisan lean is a more significant predictor; (2) States with greater level of poverty searched less frequently; (3) Leadership on the national level significantly influenced people's searching behavior; (4) Communities were most interested in "local risk" information as well as quantifiable information. We show in this work that established individual information seeking theoretical predictors (risk) can predict online collective information demand and information seeking subcategories with important contributions from collective conditions (leadership). Health communication practitioners can design health messages and choose media channels more purposefully according to what people are most interested in searching.
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Affiliation(s)
- Yiwei Xu
- Department of Communication, Cornell University
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Marshall D, McRee AL, Gower AL, Reiter PL. Views about vaccines and how views changed during the COVID-19 pandemic among a national sample of young gay, bisexual, and other men who have sex with men. Hum Vaccin Immunother 2023; 19:2281717. [PMID: 37965729 PMCID: PMC10653772 DOI: 10.1080/21645515.2023.2281717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/04/2023] [Indexed: 11/16/2023] Open
Abstract
We examined perceptions of vaccines and changes during the coronavirus disease 2019 (COVID-19) pandemic. From 2019 to 2021, a national sample of young gay, bisexual, and other men who have sex with men completed an open-ended survey item about vaccine perceptions. Analyses identified themes and polarity (negative, neutral, or positive) within responses and determined temporal changes across phases of the pandemic ("pre-pandemic," "pandemic," "initial vaccine availability," or "widespread vaccine availability"). Themes included health benefits of vaccines (53.9%), fear of shots (23.7%), COVID-19 (10.3%), vaccines being safe (5.6%), and vaccine hesitancy/misinformation (5.5%). Temporal changes existed for multiple themes (p < .05). Overall, 53.0% of responses were positive, 31.2% were negative, and 15.8% were neutral. Compared to the pre-pandemic phase, polarity was less positive for the widespread vaccine availability phase (odds ratio = 0.64, 95% confidence interval: 0.42-0.96). The findings provide insight into how vaccine perceptions change in concert with a public health emergency.
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Affiliation(s)
- Daniel Marshall
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Annie-Laurie McRee
- Division of General Pediatrics and Adolescent Health, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
- Center for Scientific Review, National Institutes of Health, Bethesda, MD, USA
| | - Amy L. Gower
- Division of General Pediatrics and Adolescent Health, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Paul L. Reiter
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
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Andía-Rodríguez I, Ayala-Laurel V, Díaz-Carrillo J, Llange-Sayan M, Picón S, Reyes-Reyes E, Armada J, Mejía CR. [Analysis with Google Trends and Our World in Data on Global Mental Health in the Context of the covid-19 Pandemic]. REVISTA COLOMBIANA DE PSIQUIATRIA 2023:S0034-7450(23)00036-7. [PMID: 37360791 PMCID: PMC10099180 DOI: 10.1016/j.rcp.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/04/2023] [Indexed: 06/28/2023]
Abstract
Introduction During the covid-19 pandemic, mental health services were unable to cope with the high demand from the population, so many people chose to search the Internet for information that could help them cope with the psychological process they were experiencing at the time. The aim of this study was to characterize the global search trend for the term «psychiatry» in the context of covid-19 using Google Trends and Our World in Data. Methods Descriptive-cross-sectional study on global search trends for Psychiatry in the context of covid-19 under the terms «psychiatry», «depression», «anxiety», «stress», «insomnia» and «suicide» in the category of health, this was conducted over the period 2020-2021 and time graphs were generated. Results The term «psychiatry» remained at a consistently high relative search volume (between 60 and 90), with a significant and gradual search in the month of April. The relative search volume for «depression», «anxiety» and «stress» remained constant with some non-significant fluctuations over the period 2020-2021. The term «insomnia» was predominant between January and June 2020, gradually declining in April and remaining constant until October 2021. Finally, the term «suicide» had a fluctuating RBV between 60 and 100 during this period. Conclusions During the study period, the topics related to mental health and the speciality of psychiatry remained constant, with some fluctuating, but not outstanding variations.
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Affiliation(s)
| | - Valeria Ayala-Laurel
- Facultad de Medicina Humana, Universidad Norbert Wiener, Lima, Peru
- Sociedad Científica de Estudiantes de Medicina, Universidad Norbert Wiener, Lima, Perú
| | | | | | - Samanta Picón
- Facultad de Medicina Humana, Universidad Norbert Wiener, Lima, Peru
| | - Eder Reyes-Reyes
- Facultad de Medicina Humana, Universidad Norbert Wiener, Lima, Peru
| | - José Armada
- Facultad de Ciencias Empresariales, Universidad Continental, Huancayo, Perú
| | - Christian R Mejía
- Facultad de Ciencias de la Salud, Universidad de Huánuco, Huánuco, Perú
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Wang B, Liang B, Chen Q, Wang S, Wang S, Huang Z, Long Y, Wu Q, Xu S, Jinna P, Yang F, Ming WK, Liu Q. COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3007. [PMID: 36833701 PMCID: PMC9958808 DOI: 10.3390/ijerph20043007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.
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Affiliation(s)
- Binhui Wang
- School of Management, Jinan University, Guangzhou 510632, China
| | - Beiting Liang
- College of Economics, Jinan University, Guangzhou 510632, China
| | - Qiuyi Chen
- School of Journalism, Fudan University, Shanghai 200433, China
| | - Shu Wang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Laboratory of Biomass and Green Technologies, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Siyi Wang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Zhongguo Huang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Yi Long
- Law School of Artificial Intelligence, Shanghai University of Political Science and Law, Shanghai 201701, China
| | - Qili Wu
- School of Journalism and Communication, Jinan University National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou 510632, China
| | - Shulin Xu
- School of Economic, Guangzhou College of Commerce, Guangzhou 511363, China
| | - Pranay Jinna
- School of Business, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Fan Yang
- Communication Department, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Wai-Kit Ming
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Liu
- School of Journalism and Communication, Jinan University National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou 510632, China
- School of Business, University at Albany, State University of New York, Albany, NY 12222, USA
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Dai S, Han L. Influenza surveillance with Baidu index and attention-based long short-term memory model. PLoS One 2023; 18:e0280834. [PMID: 36689543 PMCID: PMC9870163 DOI: 10.1371/journal.pone.0280834] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/10/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem. METHODS In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index. RESULTS The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models. CONCLUSION Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.
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Affiliation(s)
- Shangfang Dai
- School of Economics and Management, Tsinghua University, Beijing, China
| | - Litao Han
- School of Mathematics, Renmin University of China, Beijing, China
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Rovetta A. Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis. JMIRX MED 2022; 3:e35356. [PMID: 35481982 PMCID: PMC9031689 DOI: 10.2196/35356] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/15/2022] [Accepted: 03/06/2022] [Indexed: 12/14/2022]
Abstract
Background Google Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature. Objective This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends. Methods Through the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword "vaccine reservation" query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation (z) and percentage difference (δ) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported. Results Simple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min r²=0.460, P*<.001, lag 0 weeks; max r²=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (δ>55.8%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29). Conclusions This research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper.
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