Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy
We used a semi-parametric Bayesian modeling framework to characterize social and environmental covariates to COVID-19 related mortality in the Lombardy region of Italy. Bayesian profile regression creates clusters similar to unsupervised machine learning methods while also enabling statistical inference. We identified complex relationships between environmental pollutants and demographics suggesting that policy proposals must be holistic and spatially specific in order to substantially reduce COVID-19 mortality. As co-author my role involved compiling environmental data, modeling ambient pollution, and validating model output.