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Social distancing can be described as an effort to maintain a physicaldistance between individuals and has become a necessary public health measureto combat cornoavirus disease 2019 (COVID-19). Social distancing is known toweaken incidences and deaths due to COVID-19, however, there are detrimentaleconomic and psychological effects. This motivates us to analyze incidences(and deaths) of COVID-19 along with a measure of the health of the US economy(i.e., the adjusted closing price of the Dow Jones Industrial), and a measureof the public interest in COVID-19 through Google Trends data. The model weimplement is developed to be easily adapted to a data scientist's preferredmethod for continuous data, which is done to aid future analyses of thisimportant dataset. This dataset consists of multiple response types (e.g.,continuous-valued, count-valued, binomial counts). Thus, we introduce areasonable easy-to-implement all-purpose method that "converts" a statisticalmodel for continuous responses (the preferred model) into a Bayesian model formulti-response data sets. To do this, we transform the data such that thecontinuous-valued transformed data can be reasonably modeled using thepreferred model and the transformation itself is treated as unknown. Theimplementation of our approach involves two steps. The first step producesposterior replicates of the transformed data using a latent conjugatemultivariate (LCM) model. The second step involves generating values from theposterior distribution implied by the preferred model. We refer to our model asthe hierarchical generalized transformation (HGT) model. In a simulation, wedemonstrate the flexibility of the HGT model by incorporating two differentpreferred models: Bayesian additive regression trees (BART) and the spatialmixed effects (spatio-temporal mixed effects) models.

Joint spatio-temporal analysis of multiple response types using the  hierarchical generalized transformation model with application to coronavirus  disease 2019 and social distancing