A comparative study of machine learning and geospatial techniques for analyzing Dengue diffusion patterns and identifying hotspots in Bangladesh

Abstract

Dengue fever is still a major public health challenge in tropical and subtropical coun- tries, especially in Bangladesh where epidemic has been a big threat to public health.

In this paper, we have developed an integrative computational approach analyzing geographic information and employing data mining to forecast dengue spread and reveal vulnerable regions. Our reference methods include Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Lasso Regression, Elastic Net Regression, Bidirectional Long Short Term Memory (BiLSTM), and DiffFlow as well as TabDDPM. The study builds on past results, climatic parameters, and population density to improve predictive performance. The data set used in this research was collected from the official web site of Directorate General of Health Services (DGHS)

that made the data authentic. The proposed approach, as a result, provides sig- nificantly higher predictive performance than conventional statistical analysis based

on the spatial and machine learning components. Furthermore, to categorize and prioritize the high-risk areas, we apply special methods of multiple criteria decision

making – TOPSIS and VIKOR. We reveal that the proposed models based on ma- chine learning methodologies are useful for identifying dengue fever hotspot areas

and enlightening information for public health officials regarding timely application of control measures.. This study emphasises the necessity of the epidemiological and climate data coupled with the computational modeling to mitigate future outbreaks."