Dengue fever is a critical public health concern in Bangladesh, causing significant morbidity and straining healthcare systems during seasonal outbreaks. Early prediction of dengue incidence is crucial for timely intervention and resource allocation. This project leverages search query surveillance data from platforms like Google Trends to predict dengue incidence in Bangladesh. Search query data, reflecting public interest and health-related concerns, will be analyzed in conjunction with epidemiological and environmental data to develop predictive models. By identifying search patterns associated with dengue outbreaks, this approach offers a cost-effective and real-time surveillance tool.

Advanced statistical and machine learning techniques will be employed to model the relationship between search query trends and dengue incidence rates across different regions of Bangladesh. The project aims to validate the predictive accuracy of search query surveillance in capturing early signals of dengue outbreaks and provide actionable insights for public health authorities.

This study addresses a critical need for innovative, low-cost, and real-time methods to enhance dengue surveillance in resource-constrained settings. By harnessing widely accessible digital data, the project seeks to improve early warning systems for dengue outbreaks in Bangladesh, enabling timely interventions, reducing disease burden, and saving lives. The findings can inform public health strategies and policymaking, ultimately contributing to the national and global efforts to combat vector-borne diseases.