Dengue Dynamics in Bangladesh: Unveiling Insights Through Statistical and Machine Learning Analysis

Abstract

Dengue fever remains a significant public health concern in Bangladesh, with recurring outbreaks posing substantial challenges to healthcare systems and communities. This study provides a concise overview of a comprehensive study aimed at unraveling the dynamics of dengue in Bangladesh through a synergistic combination of statistical and machine learning analyses. By applying statistical techniques, we first identify temporal and spatial patterns, uncovering seasonal trends, hotspot regions, and fluctuations in dengue incidence. The trend of safe childbirth practices gradually increased between 2000 and 2023. Dhaka, the capital city of Bangladesh, and its surrounding areas in the Dhaka Division showed a high number of dengue cases and deaths. The knowledge and awareness level about dengue was significantly higher for educated respondents (OR = 1.89, 1.21–1.97), residing in semi-urban regions (OR = 1.35, 0.93–1.41), female (OR = 1.39, 1.14–1.62), living in Dhaka division (OR = 3.72, 2.89–3.88), and housewife (OR = 1.52, 1.26–1.89). This initial analysis allows us to pinpoint high-risk areas and periods, facilitating targeted intervention strategies. In tandem with traditional statistical methods, we harness the power of machine learning to develop a predictive model which is capable of forecasting dengue outbreaks with enhanced accuracy. In conclusion, this study represents a comprehensive effort to deepen our understanding of dengue dynamics in Bangladesh. By combining statistical analyses with machine learning technique, we aim to provide actionable insights that can inform public health policies and interventions. Our findings have the potential to guide the allocation of resources, improve preparedness, and ultimately mitigate the impact of dengue fever in Bangladesh, offering a valuable framework for addressing similar challenges in other regions grappling with vector-borne diseases.