Investigating the Relationship Between World Development Indicators and the Occurrence of Disease Outbreaks in the 21st Century: A Case Study
This work addresses the challenge of identifying vulnerable sectors for outbreak mitigation for civic authorities and healthcare workers, but it is incremental as it applies existing methods to new data.
The study investigated the relationship between World Development Indicators and disease outbreaks from 2000-2019 using data-driven models, finding that classification algorithms could identify vulnerable socio-economic sectors affected by outbreaks.
The timely identification of socio-economic sectors vulnerable to a disease outbreak presents an important challenge to the civic authorities and healthcare workers interested in outbreak mitigation measures. This problem was traditionally solved by studying the aberrances in small-scale healthcare data. In this paper, we leverage data driven models to determine the relationship between the trends of World Development Indicators and occurrence of disease outbreaks using worldwide historical data from 2000-2019, and treat it as a classic supervised classification problem. CART based feature selection was employed in an unorthodox fashion to determine the covariates getting affected by the disease outbreak, thus giving the most vulnerable sectors. The result involves a comprehensive analysis of different classification algorithms and is indicative of the relationship between the disease outbreak occurrence and the magnitudes of various development indicators.