The identification of spatially constrained homogeneous clusters of Covid-19 transmission

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

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 M…

探索Covid-19时空动力学：非欧几里得空间感知函数配准

Exploring Covid-19 Spatiotemporal Dynamics: Non-Euclidean Spatially Aware Functional Registration

本文提出一种非欧几里得空间感知函数配准方法，通过多维标度近似驾驶时间以解决传统空间框架失效问题，定量分析了英国380个地区Covid-19疫情波次的时空相位变化。

Spatial scales of COVID-19 transmission in Mexico

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggrega…

Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and non-homogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space…