Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.
Journal:
PLoS neglected tropical diseases
PMID:
39820842
Abstract
BACKGROUND: Bangladesh is facing a formidable challenge in mitigating waterborne diseases risk exacerbated by climate change. However, a comprehensive understanding of the spatio-temporal dynamics of these diseases at the district level remains elusive. Therefore, this study aimed to fill this gap by investigating the spatio-temporal pattern and identifying the best tree-based ML models for determining the meteorological factors associated with waterborne diseases in Bangladesh.