  Epidemiologists aiming to model the dynamics of global events face a
significant challenge in identifying the factors linked with anomalies such as
disease outbreaks. In this paper, we present a novel method for identifying the
most important development sectors sensitive to disease outbreaks by using
global development indicators as markers. We use statistical methods to assess
the causative linkages between these indicators and disease outbreaks, as well
as to find the most often ranked indicators. We used data imputation techniques
in addition to statistical analysis to convert raw real-world data sets into
meaningful data for causal inference. The application of various algorithms for
the detection of causal linkages between the indicators is the subject of this
research. Despite the fact that disparities in governmental policies between
countries account for differences in causal linkages, several indicators emerge
as important determinants sensitive to disease outbreaks over the world in the
21st Century.
