Modeling Air Pollution, Climate, and Health Data Using Bayesian Networks: A Case Study of the English Regions

The link between pollution and health is commonly explored by trying to identify the dominant cause of pollution and its most significant effect on health outcomes. The use of multivariate features to describe exposure is less explored because investigating a large domain of scenarios is theoretically (i.e., interpretation of results) and technically (i.e., computational effort) challenging. In this work we explore the use of Bayesian Networks with a multivariate approach to identify the probabilistic dependence structure of the environment-health nexus. This consists of environmental factors (topography and climate), exposure levels (concentration of outdoor air pollutants), and health outcomes (mortality rates). The information is collated with regard to a data-rich study area: the English regions (UK), which incorporate environmental types that are different in character from urban to rural. We implemented a reproducible workflow in the R programming language to collate environment-health data and analyze almost 50 millions of observations making use of a graphical model (Bayesian Network) and Big Data technologies. Results show that for pollution and weather variables the model tests well in sample but also has good predictive power when tested out of sample. This is facilitated by a training/testing split in the data along time and space dimension and suggests that the model generalizes well to new regions and time periods.