Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical
Abstract: Background A variety of obstacles including bureaucracy and
lack of resources have interfered with timely detection and reporting of dengue cases
in many endemic countries. Surveillance efforts have turned to modern data sources,
such as Internet search queries, which have been shown to be effective for monitoring
influenza-like illnesses. However, few have evaluated the utility of web search query
data for other diseases, especially those of high morbidity and mortality or where a
vaccine may not exist. In this study, we aimed to assess whether web search queries are
a viable data source for the early detection and monitoring of dengue epidemics.
Methodology/Principal Findings Bolivia, Brazil, India, Indonesia and Singapore were
chosen for analysis based on available data and adequate search volume. For each
country, a univariate linear model was then built by fitting a time series of the
fraction of Google search query volume for specific dengue-related queries from that
country against a time series of official dengue case counts for a time-frame within
2003–2010. The specific combination of queries used was chosen to maximize model fit.
Spurious spikes in the data were also removed prior to model fitting. The final models,
fit using a training subset of the data, were cross-validated against both the overall
dataset and a holdout subset of the data. All models were found to fit the data quite
well, with validation correlations ranging from 0.82 to 0.99. Conclusions/Significance
Web search query data were found to be capable of tracking dengue activity in Bolivia,
Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official
sources are often not available until after some substantial delay, web search query
data are available in near real-time. These data represent valuable complement to
assist with traditional dengue surveillance.