Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

TL;DR

This paper introduces an attributed network embedding model that captures heterogeneous features and visitation patterns across US counties to identify distinct COVID-19 spread archetypes, aiding predictive pandemic monitoring.

Contribution

It develops a novel attributed network embedding approach combining county features and visitation data to uncover pandemic spread archetypes and key influencing features.

Findings

Identified four distinct COVID-19 spread archetypes among US counties.

Key features influencing spread risk patterns were successfully extracted.

The model enhances predictive pandemic monitoring beyond traditional epidemiological models.

Abstract

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.