Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis

Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, we first examine the geographical distribution of the online behavioral variations towards Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis in the United States by year from 2004 to 2017. Next, we examine the correlations between Google Trends data and official health data from the ‘Centers for Disease Control and Prevention’ (CDC) on said diseases, followed by estimating linear regressions for the respective relationships. The results show that Infoveillance can assist with exploring public awareness and accurately measure the behavioral changes towards said diseases. The correlations between Google Trends data and CDC data on Chlamydia cases are statistically significant at a national level and in most of the states, while the forecasting exhibits good performing results in many states. For Hepatitis, significant correlations are observed for several US States, while forecasting also exhibits promising results. On the contrary, several factors can affect the applicability of this forecasting method, as in the cases of Gonorrhea, Syphilis, and Tuberculosis, where the correlations are statistically significant in fewer states. Thus this study highlights that the analysis of Google Trends data should be done with caution in order for the results to be robust. In addition, we suggest that the applicability of this method is not that trivial or universal, and that several factors need to be taken into account when using online data in this line of research. However, this study also supports previous findings suggesting that the analysis of real-time online data is important in health assessment, as it tackles the long procedure of data collection and analysis in traditional survey methods, and provides us with information that could not be accessible otherwise.

[1]  Vittoria Colizza,et al.  Risk of MERS importation and onward transmission: a systematic review and analysis of cases reported to WHO , 2016, BMC Infectious Diseases.

[2]  D. Ingram,et al.  Seasonal trends in sleep-disordered breathing: evidence from Internet search engine query data , 2015, Sleep and Breathing.

[3]  Giuseppe Lippi,et al.  Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings , 2017, Journal of epidemiology and global health.

[4]  S. Mehta,et al.  A comparison of Internet search trends and sexually transmitted infection rates using Google trends. , 2014, Sexually transmitted diseases.

[5]  S. Rutherford,et al.  Using Google Trends for Influenza Surveillance in South China , 2013, PloS one.

[6]  R T Wigand,et al.  Social media in Ebola outbreak , 2016, Epidemiology and Infection.

[7]  Andrey Zheluk,et al.  Internet Search and Krokodil in the Russian Federation: An Infoveillance Study , 2014, Journal of medical Internet research.

[8]  Alexander Domnich,et al.  Age-Related Differences in the Accuracy of Web Query-Based Predictions of Influenza-Like Illness , 2015, PloS one.

[9]  M Schootman,et al.  The utility of Google Trends data to examine interest in cancer screening , 2015, BMJ Open.

[10]  C. Schönfeldt-Lecuona,et al.  Linking Annual Prescription Volume of Antidepressants to Corresponding Web Search Query Data: A Possible Proxy for Medical Prescription Behavior? , 2015, Journal of clinical psychopharmacology.

[11]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[12]  Gabriela Ochoa,et al.  Forecasting AIDS prevalence in the United States using online search traffic data , 2018, Journal of Big Data.

[13]  L. McMahon,et al.  Using search engine query data to track pharmaceutical utilization: a study of statins. , 2010, The American journal of managed care.

[14]  G. Eysenbach.  Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet , 2009, Journal of medical Internet research.

[15]  Nicola Luigi Bragazzi,et al.  Infodemiology and Infoveillance of Multiple Sclerosis in Italy , 2013, Multiple sclerosis international.

[16]  M. Mckee,et al.  Tracking search engine queries for suicide in the United Kingdom, 2004-2013. , 2016, Public health.

[17]  Scott Loveridge,et al.  Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data. , 2017, Journal of affective disorders.

[18]  Nicola Luigi Bragazzi,et al.  Infodemiology of status epilepticus: A systematic validation of the Google Trends-based search queries , 2016, Epilepsy & Behavior.

[19]  Pietro Liò,et al.  The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks , 2016, Scientific Reports.

[20]  Enny Das,et al.  Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter , 2017, Journal of medical Internet research.

[21]  A. Ahmadvand,et al.  “Googling” for Cancer: An Infodemiological Assessment of Online Search Interests in Australia, Canada, New Zealand, the United Kingdom, and the United States , 2016, JMIR cancer.

[22]  Amaryllis Mavragani,et al.  Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics , 2016 .

[23]  Francesco Brigo,et al.  Web search behavior for multiple sclerosis: An infodemiological study. , 2014, Multiple sclerosis and related disorders.

[24]  Jieping Ye,et al.  Tuberculosis Surveillance by Analyzing Google Trends , 2011, IEEE Transactions on Biomedical Engineering.

[25]  J Agoues Martínez,et al.  [Sexually transmitted diseases. STDs]. , 1988, Revista de enfermeria.

[26]  F. Bazzoli,et al.  Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts , 2017, JMIR public health and surveillance.

[27]  Daniel Dajun Zeng,et al.  Tracking Dabbing Using Search Query Surveillance: A Case Study in the United States , 2016, Journal of medical Internet research.

[28]  Mauricio Santillana,et al.  Accurate estimation of influenza epidemics using Google search data via ARGO , 2015, Proceedings of the National Academy of Sciences.

[29]  L Charles Bailey,et al.  Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study , 2018, Journal of medical Internet research.

[30]  Amaryllis Mavragani,et al.  Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era , 2018, JMIR public health and surveillance.

[31]  Emmanuel Haffen,et al.  Google Trends®: Ready for real-time suicide prevention or just a Zeta-Jones effect? An exploratory study , 2015, Psychiatry Research.

[32]  Shilu Tong,et al.  Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases , 2016, Scientific reports.

[33]  Daniel Dajun Zeng,et al.  Information Seeking Regarding Tobacco and Lung Cancer: Effects of Seasonality , 2015, PloS one.

[34]  Hsiao-Wei Yu,et al.  Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan , 2015, Journal of medical Internet research.

[35]  Leana S. Wen,et al.  Measles, social media and surveillance in Baltimore City , 2017, Journal of public health.

[36]  Mauricio Santillana,et al.  Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak , 2016, JMIR public health and surveillance.

[37]  Amaryllis Mavragani,et al.  YES or NO: Predicting the 2015 GReferendum results using Google Trends , 2016 .

[38]  Rok Sosic,et al.  Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis , 2018, JMIR public health and surveillance.

[39]  S. Nuti,et al.  The Use of Google Trends in Health Care Research: A Systematic Review , 2014, PloS one.

[40]  Michael S. Deiner,et al.  Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data , 2018, JMIR public health and surveillance.

[41]  Man-pui Sally Chan,et al.  Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States , 2018, JMIR public health and surveillance.

[42]  Gabriela Ochoa,et al.  The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak , 2018, Big Data Cogn. Comput..

[43]  Miguel A. Vadillo,et al.  Researching Mental Health Disorders in the Era of Social Media: Systematic Review , 2017, Journal of medical Internet research.

[44]  Daniela Amicizia,et al.  Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes , 2015 .

[45]  Nicola Luigi Bragazzi,et al.  How often people google for vaccination: Qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends , 2017, Human vaccines & immunotherapeutics.

[46]  Monica Vichi,et al.  A Google-based approach for monitoring suicide risk , 2016, Psychiatry Research.