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

Abstract Number:

3182

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Paper

Participants:

Miles Moran (1), Robert Trangucci (1)

Institutions:

(1) Oregon State University, N/A

Co-Author:

Robert Trangucci
Oregon State University

First Author:

Miles Moran
Oregon State University

Presenting Author:

Miles Moran
Oregon State University

Abstract Text:

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.

Keywords:

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Disease Prediction

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