Table 3.
Methods of exploring correlations using Google Trends in health assessment.
| Number | Authors | Method | Description |
| 1 | Alicino et al, 2015 [85] | Pearson correlation | Ebola-related Google Trends data with Ebola cases |
| 2 | Arora et al, 2016 [81] | Spearman correlation | Suicide search activity vs official suicide rates (and per age) |
| 3 |
Bakker et al, 2016 [96] | Correlations | Between Google Trends data and reported cases |
| 4 | Bragazzi et al, 2016 [99] | Pearson correlation | Between Google Trends data and epidemiological data |
| 5 | Bragazzi, 2013 [98] | Autocorrelation; Pearson correlation | For the time series for multiple sclerosis (MS); between MS terms |
| 6 | Bragazzi et al, 2016 [101] | Autocorrelation; Partial Autocorrelation | To compute correlation of the time series with its own values |
| 7 | Bragazzi et al, 2016 [102] | Pearson correlation | Status epilepticus terms with etiology and management related terms |
| 8 | Bragazzi et al, 2016 [43] | Pearson correlation | Google searches for Silicosis with Normalized Google News, Google Scholar, PubMed Publications, Twitter traffic, Wikipedia |
| 9 | Bragazzi et al, 2016 [63] | Pearson correlation | Among Google Trends data and other data generating sources |
| 10 | Bragazzi, 2014 [103] | Pearson correlation; autocorrelation and partial autocorrelation | Nonsuicidal self-injury and related terms; nonsuicidal self-injury plots showed regular cyclical pattern |
| 11 | Cavazos-Regh et al, 2015 [107] | Pearson correlation | Among Google Trends data for noncigarette tobacco and prevalence |
| 12 | Cho et al, 2013 [78] | Pearson correlation | Google flu-related queries with surveillance data for different influenza seasons |
| 13 | Crowson et al, 2016 [93] | Pearson correlation | Between the selected keywords. Between medical prescriptions data and Google Trends data |
| 14 | Deiner et al, 2016 [70] | Spearman correlation | For correlating seasonality of clinical diagnoses with Google Trends data |
| 15 | Domnich et al, 2015 [79] | Pearson correlation | Among the examined search terms and influenza-like illness |
| 16 | Foroughi et al, 2016 [115] | Rank correlations; cross-country correlations; Pearson correlations | For search volumes; for the search volumes for cancer; for the weekly search volumes between countries |
| 17 | Gahr et al, 2015 [75] | Pearson correlation | Among annual prescription volumes and Google Trends data |
| 18 | Gamma et al, 2016 [90] | Cross-correlations | Cross-correlations between search volumes and crime statistics |
| 19 | Gollust et al, 2016 [117] | Multinomial Logit Models | To relate health insurance rates |
| 20 | Guernier et al, 2016 [82] | Spearman correlation; cross-correlation | Correlating the examined search terms with notifications of tick paralysis cases record; with lag values from −7 to +7 months |
| 21 | Hassid et al, 2016 [120] | Pearson correlation | Between Google Trends data and National Inpatient Sample data |
| 22 | Johnson et al, 2014 [84] | Pearson correlation | Pearson correlations to explore the relation of Google Trends data and sexually transmitted infection reported rates |
| 23 | Kang et al, 2013 [77] | Pearson correlation | To explore the association of (and among) search terms with surveillance data |
| 24 | Kang et al, 2015 [72] | Spearman correlation | Google Trends data for allergic rhinitis and related Google Trends terms and real world epidemiologic data for the United States |
| 25 | Koburger et al, 2015 [65] | Spearman-Brown correlation | To explore relations among Google Trends data and railway suicides |
| 26 | Ling and Lee, 2016 [126] | Pearson correlation | Between disease prevalence and Google Trends data |
| 27 | Mavragani et al, 2016 [76] | Pearson correlation | Between Google Trends data and published papers and Google Trends data with prescriptions |
| 28 | Phelan et al, 2016 [133] | Linear Regression | To examine if there is significant correlation between searches and time |
| 29 | Poletto et al, 2016 [56] | Pearson correlation | Between Google Trends data and number of alerts published by ProMED mail and the number of Disease Outbreak News published by the World Health Organization |
| 30 | Pollett et al, 2015 [91] | Pearson correlation | To shortlist related search terms to pertussis |
| 31 | Rohart et al, 2016 [135] | Spearman rank correlations; Spearman correlation; cross-correlations | For the diseases examined; correlations between diseases and the investigated search metrics; to identify best lags |
| 32 | Shin et al, 2016 [137] | Spearman correlation | Between Google Trends data and the number of confirmed cases of Middle East Respiratory Syndrome and for quarantined cases of Middle East Respiratory Syndrome |
| 33 | Schootman et al, 2015 [45] | Pearson correlation | Between Respiratory Syncytial Virus and Behavioral Risk Factor Surveillance System prevalence data for 5 cancer screening tests |
| 34 | Schuster et al, 2010 [73] | Correlations | Lipitor Google Trends data and Lipitor revenues |
| 35 | Sentana-Lledo et al, 2016 [138] | Kendall’s Tau-b test | To explore the correlation of Google Trends data with paper interview survey results |
| 36 | Simmering et al, 2014 [50] | Cross-correlations | Between Google Trends data for drugs and drug utilization, to see changes in search volumes following knowledge events |
| 37 | Solano et al, 2016 [80] | Correlations; cross-correlations | Between Google Trends data for suicide and national suicide rates; between different search terms |
| 38 | Wang et al, 2015 [92] | Pearson correlation | Between Google Trends data and new dementia cases |
| 39 | Willson et al, 2015 [86] | Spearman correlation | Between Google Trends data and observed data for aeroallergens |
| 40 | Zhang et al, 2015 [71] | Cross-correlations | To examine linear and temporal associations of the seasonal data |
| 41 | Zhang et al, 2016 [51] | Pearson correlation | To study pairwise comparisons among searches for different terms in Google Trends |