Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax

TL;DR

This paper introduces Deep Graph Diffusion Infomax (DGDI), a novel model for predicting locations visited by COVID-19 cases using limited data, addressing privacy issues and outperforming existing methods.

Contribution

The paper presents DGDI, a new deep learning model that jointly captures geometric graphs, diffusions, and locations for micro-level COVID-19 mobility prediction.

Findings

DGDI significantly outperforms competing methods in benchmarks.

The model effectively handles data sparsity under privacy constraints.

Two new benchmarks facilitate COVID-19 mobility research.

Abstract

Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and then studied the effectiveness of NPIs. In this work, we focus on mobility modeling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a micro perspective prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph. we propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.