Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance
Venue
PLoS Neglected Tropical Diseases, vol. 5 Issue 5 (2011)
Publication Year
2011
Authors
Emily H. Chan, Vikram Sahai, Corrie Conrad, John S. Brownstein
BibTeX
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.
