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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport.

Frank Nielsen¹, Gautier Marti², Sumanta Ray³

¹Sony Computer Science Laboratories Inc, Tokyo, Japan.

Sankhya. Series B (2008)

|March 22, 2021

Summary

This study introduces a computational framework to analyze COVID-19 spread by comparing human mobility and case data across 150+ US cities. It identifies distinct clusters of cities with similar temporal patterns, aiding pandemic response strategies.

Keywords:

COVID-19.Clustering Mobility Optimal transport Time series Wasserstein distance

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Area of Science:

Computational epidemiology
Public health analytics
Socioeconomic impact analysis

Background:

Social distancing and stay-at-home orders are crucial for pandemic control.
The dynamic relationship between mobility, interventions, and disease incidence varies across populations.
Understanding these temporal dependencies is vital for effective public health strategies.

Purpose of the Study:

To develop and apply a computational framework for measuring and comparing temporal relationships between human mobility and COVID-19 cases.
To analyze these relationships across over 150 US cities.
To identify clusters of cities with similar temporal dependencies and analyze their socioeconomic composition.

Main Methods:

Utilized a novel application of Optimal Transport to compute distances between normalized bivariate time series of mobility and COVID-19 cases.
Applied clustering techniques to group cities based on temporal dependencies.
Computed Wasserstein barycenters to represent cluster-specific dynamic patterns.
Analyzed cluster composition using city-specific socioeconomic covariates.

Main Results:

Identified 10 distinct clusters of US cities exhibiting similar temporal dependencies between human mobility and COVID-19 incidence.
Characterized the overall dynamic patterns for each cluster using Wasserstein barycenters.
Revealed variations in cluster composition based on socioeconomic factors, suggesting differential impacts of mobility on disease spread.

Conclusions:

The developed framework effectively measures and compares complex temporal relationships between human mobility and disease incidence.
Clustering cities based on these dynamics reveals distinct patterns of pandemic spread across different urban environments.
Socioeconomic factors play a significant role in shaping the relationship between mobility and COVID-19 cases, informing targeted public health interventions.