A0421
Title: Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information
Authors: Paula Moraga - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) [presenting]
Abstract: Dengue is a mosquito-borne viral disease that poses significant public health challenges in tropical and sub-tropical regions worldwide. Effective surveillance systems are essential for dengue prevention and control. However, traditional systems rely on delayed data, limiting their effectiveness. The value of using Google Trends data to complement official dengue data is evaluated for nowcasting dengue in Brazil, a country frequently affected by this disease. Various nowcasting approaches are compared 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 additional insights into dengue activity levels. To support real-time decision-making, 'Dengue-Tracker' is also presented, a website that displays weekly nowcasts to inform both decision-makers and the public, improving situational awareness of dengue activity. In conclusion, the value of digital data sources in enhancing dengue nowcasting is demonstrated, and the importance of integrating alternative data streams into traditional surveillance systems is emphasized for better-informed decision-making.
