Cargando…
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: | 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 |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
|
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 |
Ejemplares similares
-
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
por: Talekar, Alok, et al.
Publicado: (2020) -
Data-driven modeling for different stages of pandemic response
por: Adiga, Aniruddha, et al.
Publicado: (2020) -
Data-Driven Modeling for Different Stages of Pandemic Response
por: Adiga, Aniruddha, et al.
Publicado: (2020) -
MATHEMATICAL MODELS FOR COVID-19 PANDEMIC: A COMPARATIVE ANALYSIS
por: ADIGA, ANIRUDDHA, et al.
Publicado: (2020) -
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
por: Adiga, Aniruddha, et al.
Publicado: (2020)