Using Mobility Data to Understand and Forecast COVID19 Dynamics
Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2020 |
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| Enthalten in: |
medRxiv : the preprint server for health sciences - (2020) vom: 15. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Lijing [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Revised 03.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2020.12.13.20248129 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM319191303 |
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| 520 | |a Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines | ||
| 650 | 4 | |a Preprint | |
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| 700 | 1 | |a Adiga, Aniruddha |e verfasserin |4 aut | |
| 700 | 1 | |a Sadilek, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Tendulkar, Ashish |e verfasserin |4 aut | |
| 700 | 1 | |a Venkatramanan, Srinivasan |e verfasserin |4 aut | |
| 700 | 1 | |a Vullikanti, Anil |e verfasserin |4 aut | |
| 700 | 1 | |a Aggarwal, Gaurav |e verfasserin |4 aut | |
| 700 | 1 | |a Talekar, Alok |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jiangzhuo |e verfasserin |4 aut | |
| 700 | 1 | |a Lewis, Bryan |e verfasserin |4 aut | |
| 700 | 1 | |a Swarup, Samarth |e verfasserin |4 aut | |
| 700 | 1 | |a Kapoor, Amol |e verfasserin |4 aut | |
| 700 | 1 | |a Tambe, Milind |e verfasserin |4 aut | |
| 700 | 1 | |a Marathe, Madhav |e verfasserin |4 aut | |
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