Dengue nowcasting in Brazil by combining official
surveillance data and Google Trends information
Yang Xiao, Guilherme Soares, Leonardo Bastos, Rafael Izbicki, Paula
Moraga
Link: https://www.medrxiv.org/content/10.1101/2024.09.02.24312934v1
Abstract:
Dengue is a mosquito-borne viral disease that poses significant public
health challenges in tropical and sub-tropical regions worldwide.
Surveillance systems are essential for dengue prevention and control.
However, traditional systems often rely on delayed data, limiting their
effectiveness. To address this, nowcasting methods are needed to
estimate underreported cases, enabling more timely decision-making. This
study evaluates the value of using Google Trends indices of
dengue-related keywords to complement official dengue data for
nowcasting dengue in Brazil, a country frequently affected by this
disease. We compare various nowcasting approaches that incorporate
autoregressive features from official dengue cases, Google Trends data,
and a combination of both, using a naive approach as a baseline. The
performance of these methods is evaluated by nowcasting weekly dengue
cases from March to June 2024 across Brazilian states. Error measures
and 95% coverage probabilities reveal that models incorporating Google
Trends data enhance the accuracy of weekly nowcasts across states and
offer valuable insights into dengue activity levels. To support
real-time decision-making, we also present Dengue Tracker, a website
that displays weekly dengue nowcasts and trends to inform both
decision-makers and the public, improving situational awareness of
dengue activity. In conclusion, the study demonstrates the value of
digital data sources in enhancing dengue nowcasting, and emphasizes the
value of integrating alternative data streams into traditional
surveillance systems for better-informed decision-making.