Abstract

Chikungunya, a vector-borne disease transmitted by Aedes mosquitoes, poses a significant global health threat. Surveillance efforts traditionally rely on clinical data, but the integration of climate and biosurveillance data offers a promising approach to enhance early detection and response strategies. This study explores the synergy between climate variables and biosurveillance data in the context of Chikungunya surveillance. Leveraging advanced analytical techniques, we investigate the correlation between meteorological factors, vector abundance, and disease incidence. By combining diverse datasets, we aim to develop a robust predictive model for Chikungunya outbreaks, enabling proactive public health interventions. The integration of climate and biosurveillance data not only enhances the accuracy of forecasting but also provides a comprehensive understanding of the environmental determinants influencing disease dynamics. This interdisciplinary approach holds the potential to revolutionize Chikungunya surveillance, offering a more effective and timely response to mitigate the impact of the disease on vulnerable populations.