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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 i...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755147/ https://www.ncbi.nlm.nih.gov/pubmed/33354685 http://dx.doi.org/10.1101/2020.12.13.20248129 |
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author | Wang, Lijing Ben, Xue Adiga, Aniruddha Sadilek, Adam Tendulkar, Ashish Venkatramanan, Srinivasan Vullikanti, Anil Aggarwal, Gaurav Talekar, Alok Chen, Jiangzhuo Lewis, Bryan Swarup, Samarth Kapoor, Amol Tambe, Milind Marathe, Madhav |
author_facet | Wang, Lijing Ben, Xue Adiga, Aniruddha Sadilek, Adam Tendulkar, Ashish Venkatramanan, Srinivasan Vullikanti, Anil Aggarwal, Gaurav Talekar, Alok Chen, Jiangzhuo Lewis, Bryan Swarup, Samarth Kapoor, Amol Tambe, Milind Marathe, Madhav |
author_sort | Wang, Lijing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7755147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-77551472020-12-23 Using Mobility Data to Understand and Forecast COVID19 Dynamics Wang, Lijing Ben, Xue Adiga, Aniruddha Sadilek, Adam Tendulkar, Ashish Venkatramanan, Srinivasan Vullikanti, Anil Aggarwal, Gaurav Talekar, Alok Chen, Jiangzhuo Lewis, Bryan Swarup, Samarth Kapoor, Amol Tambe, Milind Marathe, Madhav medRxiv Article 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. Cold Spring Harbor Laboratory 2020-12-15 /pmc/articles/PMC7755147/ /pubmed/33354685 http://dx.doi.org/10.1101/2020.12.13.20248129 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Wang, Lijing Ben, Xue Adiga, Aniruddha Sadilek, Adam Tendulkar, Ashish Venkatramanan, Srinivasan Vullikanti, Anil Aggarwal, Gaurav Talekar, Alok Chen, Jiangzhuo Lewis, Bryan Swarup, Samarth Kapoor, Amol Tambe, Milind Marathe, Madhav Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title | Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title_full | Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title_fullStr | Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title_full_unstemmed | Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title_short | Using Mobility Data to Understand and Forecast COVID19 Dynamics |
title_sort | using mobility data to understand and forecast covid19 dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755147/ https://www.ncbi.nlm.nih.gov/pubmed/33354685 http://dx.doi.org/10.1101/2020.12.13.20248129 |
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