Google Trends Data on Dengue (Indonesia)

Abstract

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. Since no previous study exists on validating official dengue reports and Google Trends data in Indonesia and comparing them over time, this study aimed to cover this gap. This research was a quantitative study using time series data (2012-2016). Two sets of data were validated using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the association between those data. Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for 3 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. 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.