Dengue hospitalizations in Brazil: Forecasting with climatic and physicians’ digital search data under real-world reporting delays

Timely forecasting of dengue hospitalizations is essential for public health preparedness but is frequently limited by delays in official reporting systems. While climatic variables are known to influence dengue transmission and can be obtained in near-real time, hospitalization data often become available only weeks after patient admission, reducing their value for early response. Digital information generated during clinical practice, such as physicians’ search patterns, may provide a complementary and more timely signal of emerging disease activity. This study evaluates whether integrating climate data with real-time records of physicians’ searches for dengue-related information improves short-term forecasts of dengue hospitalizations in Brazil under both ideal and realistic reporting conditions. Three complementary data sources were combined to generate forecasts across multiple geographic regions: weekly hospitalization counts, climatic indicators, and anonymized physician search records from a widely used clinical decision-support platform. Model performance was compared under two scenarios: one assuming immediate availability of hospitalization data and another incorporating typical reporting delays. When hospitalization data were timely, simpler model configurations — particularly those relying on hospitalization history alone or combined with climate — achieved the highest predictive accuracy, indicating that the temporal structure of the outcome itself carried substantial forecasting value. Under realistic reporting delays, however, models incorporating physicians’ search behavior consistently outperformed all other approaches across most regions. In several regions, increases in physician search activity preceded or coincided with rises in hospital admissions, indicating early clinical engagement with dengue cases. These findings indicate that physician search behavior constitutes a valuable real-time indicator of dengue activity. Integrating digital clinical behavior with climate data enhances forecasting performance under real-world reporting constraints and may strengthen early-warning systems and public health decision-making for dengue and other climate-sensitive diseases.Author summary: Dengue is a major public health challenge in Brazil, where large outbreaks place sudden pressure on health services. Although climate conditions influence dengue transmission, public health responses often rely on hospitalization data that become available only weeks or months after patients are admitted, limiting the ability to act early. In this study, we explored whether real-time digital information generated by physicians could help overcome this delay. We combined weather data with anonymized records of physicians’ searches for dengue-related information within a widely used clinical decision-support platform in Brazil. We then tested whether these digital signals could improve short-term forecasts of dengue hospitalizations across different regions of the country, especially when official hospital data were delayed. We found that when hospitalization data were available without delays, simpler models based on hospitalization history and climate patterns performed well. However, under realistic reporting delays, models that incorporated physicians’ search behavior produced more accurate forecasts across most regions. These findings show that digital clinical behavior can provide early insight into rising disease activity and support more timely public health responses.