A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19

TL;DR

This paper introduces a spatio-temporal Dirichlet process mixture model to analyze COVID-19 spread in urban areas, enabling detection of epidemic clusters, estimation of their ranges, and assessment of landmark impacts for better public health responses.

Contribution

The novel model captures disease dynamics over space and time, detects unobserved clusters, and evaluates landmark effects, improving understanding of COVID-19 spread mechanisms.

Findings

Identified epidemic clusters and their spatial-temporal ranges.

Quantified the impact of city landmarks on disease spread.

Provided a computationally efficient method for dynamic disease modeling.

Abstract

Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space-time range of the clusters that are useful to construct a warning system. Furthermore, our model can measure the impact of different types of landmarks in the city, which provides an intuitive explanation of disease spreading sources from different time points. To efficiently capture the temporal dynamics of the disease patterns, we employ a sequential approach that uses the posterior distribution of the parameters for the previous time step as the prior information for the current time step. This approach enables us to incorporate time dependence into our model in a computationally efficient manner without complicating the model structure. We also develop a model assessment by comparing the data with theoretical densities, and outline the goodness-of-fit of our fitted model.