Spatio-Temporal Modelling of Racial/Ethnic Disparities in COVID-19 with Nonrandom-Missing Covariates

Oregon Convention Center 

While panel data of disease incidence are more plentiful than ever, utilizing these data to promote equitable decision-making requires careful modelling of subpopulation-level disparities. In these kinds of tasks, e.g., comparing the trajectory of the COVID-19 pandemic between different racial/ethnic groups, a prevailing challenge is the presence of nonignorable missingness in the demographic covariates of interest. Unfortunately, most spatio-temporal models used in epidemiology inaccurately assume unobserved covariates are missing-at-random (MAR), and most missingness-process models are not spatio-temporal in nature. We respond to this issue with a Bayesian methodology for spatio-temporal modelling the joint distribution of disease counts and discrete-covariate missingness. We also demonstrate the advantage of our model using a simulation study. Finally, we apply the model to COVID-19 incidence data collected in Michigan to describe racial/ethnic disparities in the progression of the COVID-19 pandemic.

Count Data Modelling

MNAR Covariates

Bayesian Inference

Spatio-Temporal Disease Modelling

Infectious-Disease 

Section on Statistics in Epidemiology