  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.
