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

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.