Forecasting AIDS prevalence in the United States using online search traffic data

Abstract

Big Data, characterized by large volumes, high processing speed, and wide variety of datasets [1-3], have been shown to be very valuable in health care research, with Health Informatics being the field in which big data analytics have been extensively applied [4]. A popular way of addressing the challenge of Big Data is the analysis of online search traffic data [5, 6], mainly with data from Google Trends [7]. Over the past decade, this field of research, i.e., analyzing online search traffic data, has been widely used and is growing in popularity for assessing various topics, though it has mostly focused on the fields of Health and Medicine [8]. Many studies on the subject have empirically shown that Google Trends' data are related to public health data. Topics that have been explored up to this point include the analysis, assessment, and prediction of epidemics and outbreaks, as, for example, Ebola [9, 10], Measles [11], the Bed-Bug epidemic [12], and Tuberculosis [13]. A much studied

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

“…It is evident that the online interest in the term 'Chlamydia' is significant throughout the examined period, i.e., from 2004 to 2017. In the US, the top related searches for the term 'Chlamydia' from 2004 to 2016 include: 'chlamydia symptoms' (100), 'chlamydia gonorrhea' (50), 'symptoms of chlamydia' (38), 'chlamydia men' (36), 'std chlamydia' (34), 'std' (33), 'chlamydia treatment' (33), 'treatment chlamydia' (33), 'chlamydia in men' (28), 'chlamydia infection' (26), 'chlamydia in women' (25), 'what is chlamydia' (24), 'chlamydia test' (22), 'chlamydia symptoms women' (19), 'chlamydia symptoms men' (18), 'chlamydia symptoms in women' (16), 'chlamydia symptoms in men' (16), 'chlamydia discharge' (15), 'chlamydia signs' (14), 'chlamydia cure' (13). Table 1 consists of the Pearson correlation coefficients between Google Trends data on the term 'Chlamydia' and official Chlamydia cases in each US State from 2004 to 2016.…”

“…This database contains data for 6 infectious diseases, i.e., HIV/AIDS, Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis. Following the well performing forecasting results for AIDS [18], in this study we use data on the rest of the diseases included in AtlasPlus. The data retrieved for Hepatitis are from January 1st, 2004 to December 31st, 2015, while for the rest of the examined diseases; the examined time frame is from January 1st, 2004 to December 31st, 2016.…”

“…Google Trends data main advantages are that they are real-time data, and that they provide us with the revealed and not the stated preferences [9]. Google Trends has been a useful tool for the analysis, monitoring, forecasting, and nowcasting of many health topics; in seasonal [2,10], chronic [11][12][13][14], and infectious diseases [15][16][17], as well as in outbreaks and epidemics, such as in AIDS [18], Measles [19], Ebola [20,21], MERS [22], and the Zika Virus [23][24][25]. Online queries have been much employed up to this point for the analysis and forecasting of Influenza Like Illness, i.e., the flu [6,[26][27][28], while an emerging interest in analyzing Google queries for vaccination related topics has been increasing over the last couple of years [19,[29][30][31].…”

Predicting referendum results in the Big Data Era

“…In addition, a limitation of the tool is that it has been observed that data retrieved on different time points for the same time-frame may slightly vary. Despite the above, many studies in various subjects have shown that indeed Google data can be used to analyze or predict behavioral variations [4,5,8,13,16,23,26,[90][91][92] and that empirical relationships exist between online search traffic data and human behavior [93,94].…”

Searching for safety: crime prevention in the era of Google

“…Thus, a strength of Google search data is that it allows researchers to access data about controversial or sensitive topics that might not be as readily obtained in traditional methodologies such as survey research (Stephens-Davidowitz 2014). To date, researchers have analyzed Internet query data for a variety of topics, including: difficult to survey populations (Chykina and Crabtree 2018), social movements (Gross and Mann 2017), political behavior (Ma-Kellams et al 2017; Stephens-Davidowitz 2014), health-seeking behavior (Nuti et al 2014;Telfer and Obradovich 2017;Zhang et al 2018), consumer behavior (Goel et al 2010), weather and disaster related concerns (Sherman-Morris et al 2011), suicide surveillance (Ayers et al 2017;Gunn and Lester 2013;Hagihara et al 2012;Ma-Kellams et al 2016;Sueki 2011), AIDS (Mavragani and Ochoa 2018), pornography (Makin and Morczek 2015;Whitehead and Perry 2018), and illicit drug use (Gamma et al 2016). Internet searches provide an innovative way to learn about crime prevention.…”