Spatial homogeneity learning for spatially correlated functional data with application to COVID-19 Growth rate curves

TL;DR

This paper introduces a novel spatially-aware functional data grouping method using CAR and Chinese restaurant process priors to analyze COVID-19 growth rate curves, capturing spatial heterogeneity and correlations.

Contribution

It develops a new geographically detailed functional data grouping approach with spatial priors, enabling detection of both contiguous and discontiguous spatial groups in pandemic data.

Findings

The method outperforms existing approaches in simulations.

It successfully identifies spatial heterogeneity in COVID-19 growth rates.

Application to US data reveals meaningful regional groupings.

Abstract

We study the spatial heterogeneity effect on regional COVID-19 pandemic timing and severity by analyzing the COVID-19 growth rate curves in the United States. We propose a geographically detailed functional data grouping method equipped with a functional conditional autoregressive (CAR) prior to fully capture the spatial correlation in the pandemic curves. The spatial homogeneity pattern can then be detected by a geographically weighted Chinese restaurant process prior which allows both locally spatially contiguous groups and globally discontiguous groups. We design an efficient Markov chain Monte Carlo (MCMC) algorithm to simultaneously infer the posterior distributions of the number of groups and the grouping configuration of spatial functional data. The superior numerical performance of the proposed method over competing methods is demonstrated using simulated studies and an application to COVID-19 state-level and county-level data study in the United States.