Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020

TL;DR

This paper introduces Mobility-GCN, a graph convolutional network leveraging human mobility data to analyze and track the spatial spread of synthetic opioid-related deaths in the USA from 2013 to 2020.

Contribution

It presents a novel graph neural network model that incorporates human mobility data to understand the spatial dynamics of synthetic opioid spread.

Findings

The model effectively captures the spread patterns of synthetic opioids.

Human mobility significantly influences the spatial distribution of opioid deaths.

The approach improves understanding of opioid crisis dynamics.

Abstract

Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.S. We analyzed the relationship between the spatiotemporal pattern of synthetic opioid-involved deaths and another key opioid, heroin, and compared patterns of deaths involving these two types of drugs during this period. Spatial connections and human mobility between counties were incorporated into a graph convolutional neural network model to represent and analyze the spread of synthetic opioid-involved deaths in the context of previous heroin-involved death patterns.