  Social distancing can be described as an effort to maintain a physical
distance between individuals and has become a necessary public health measure
to combat cornoavirus disease 2019 (COVID-19). Social distancing is known to
weaken incidences and deaths due to COVID-19, however, there are detrimental
economic 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 measure
of the public interest in COVID-19 through Google Trends data. The model we
implement is developed to be easily adapted to a data scientist's preferred
method for continuous data, which is done to aid future analyses of this
important dataset. This dataset consists of multiple response types (e.g.,
continuous-valued, count-valued, binomial counts). Thus, we introduce a
reasonable easy-to-implement all-purpose method that "converts" a statistical
model for continuous responses (the preferred model) into a Bayesian model for
multi-response data sets. To do this, we transform the data such that the
continuous-valued transformed data can be reasonably modeled using the
preferred model and the transformation itself is treated as unknown. The
implementation of our approach involves two steps. The first step produces
posterior replicates of the transformed data using a latent conjugate
multivariate (LCM) model. The second step involves generating values from the
posterior distribution implied by the preferred model. We refer to our model as
the hierarchical generalized transformation (HGT) model. In a simulation, we
demonstrate the flexibility of the HGT model by incorporating two different
preferred models: Bayesian additive regression trees (BART) and the spatial
mixed effects (spatio-temporal mixed effects) models.
