Summary of 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, by Zhiyue Xia et al.
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
by Zhiyue Xia, Kathleen Stewart
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Physics and Society (physics.soc-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study examines the spread of synthetic opioids in the US from 2013-2020, focusing on their relationship with heroin. The research analyzes the spatiotemporal pattern of synthetic opioid-involved deaths and compares it to that of heroin-involved deaths during this period. A graph convolutional neural network model is used to incorporate spatial connections and human mobility between counties to understand the spread of synthetic opioids in the context of previous heroin use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how synthetic opioids spread across the US from 2013-2020. It compares when and where these drugs were involved in deaths with when and where heroin was involved. The researchers used a special kind of computer model to understand how people moving between counties affected the spread of synthetic opioids. |
Keywords
» Artificial intelligence » Neural network » Spatiotemporal
