Abstract
ABSTRACTBackground: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia.Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report.Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data.Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak.Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.
Highlights
Digital traces are rapidly used for health monitoring purposes in recent years
The definition of digital epidemiology according to Salathe [1] is: ‘Digital epidemiology is epidemiology that uses data that was generated outside the public health system, i.e. with data that was not generated with the primary purpose of doing epidemiology.’
Cases with confirmed status of dengue from laboratory tests that were reported in official dengue reports from 34 provinces in Indonesia are available on a monthly basis
Summary
Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. As the Internet penetration becomes more widespread, with increased mobile phone usage, and the growing artificial intelligence of machine learning, the field of digital epidemiology provides a promising approach to assist traditional surveillance systems [1,2]. This approach potentially fills the gap in conventional surveillance systems in developing countries that often suffer from underreporting, limited timeliness, and the lack of sufficient budget for physical needs, facilities, and infrastructures [3,4,5,6]. Data provided by conventional surveillance system often required weeks or months to be collected
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