Investigating the Relationship Between World Development Indicators and the Occurrence of Disease Outbreaks in the 21st Century: A Case Study

TL;DR

This study uses data-driven classification models to explore how various World Development Indicators relate to disease outbreaks globally from 2000 to 2019, aiming to identify vulnerable socio-economic sectors.

Contribution

It introduces a novel application of CART-based feature selection to identify sectors affected by disease outbreaks using worldwide development data.

Findings

Certain development indicators are strongly associated with outbreak occurrence.

Classification algorithms can effectively predict outbreak vulnerability based on socio-economic data.

The study highlights key sectors at risk during disease outbreaks.

Abstract

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.