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Data-Driven of spatio-temporal model of COVID-19
This research project aims to integrate the data available of COVID-19 outbreak with partial differential Spatio-temporal model.
Keywords: data assimilation; uncertainty quantification; epidemiological model; deep learning
The outbreak of COVID-19 in 2020 has caused widespread disruption throughout the world, leading to substantial damage in human lives and economic cost. To arrest the spread of the
disease and its recurrence, governments have enacted unprecedented measures, including quarantines, lockdowns, and suspension of travel and restrict social meeting. In the last few months, over 1,000 COVID-19 articles on MedRXiv address the modelling of this outbreak. The present project will focus on the recent Spatio-temporal model introduced by Viguere et al (2020), aiming to integrate time series data into the model. The project will address the cutting-edge data-assimilation approaches: multi-fidelity, (inverse) physics-based machine learning; discrete/variational data-assimilation to tackle the challenges.
References:
Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas J.R. Hughes, Alessia Patton, Alessandro Reali, Thomas E. Yankeelov, Alessandro Veneziani,
Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion, Applied Mathematics Letters,
Volume 111, 2021.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational Physics 378, 686-707
The outbreak of COVID-19 in 2020 has caused widespread disruption throughout the world, leading to substantial damage in human lives and economic cost. To arrest the spread of the disease and its recurrence, governments have enacted unprecedented measures, including quarantines, lockdowns, and suspension of travel and restrict social meeting. In the last few months, over 1,000 COVID-19 articles on MedRXiv address the modelling of this outbreak. The present project will focus on the recent Spatio-temporal model introduced by Viguere et al (2020), aiming to integrate time series data into the model. The project will address the cutting-edge data-assimilation approaches: multi-fidelity, (inverse) physics-based machine learning; discrete/variational data-assimilation to tackle the challenges.
References: Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas J.R. Hughes, Alessia Patton, Alessandro Reali, Thomas E. Yankeelov, Alessandro Veneziani, Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion, Applied Mathematics Letters, Volume 111, 2021. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations M Raissi, P Perdikaris, GE Karniadakis Journal of Computational Physics 378, 686-707
Data-centric integration into spatio-temporal model of COVID-19.
Data-centric integration into spatio-temporal model of COVID-19.
Each year the IDEA League offers the students of its partner universities over 180 monthly grants for a short-term research exchange. In general, these grants are awarded based on academic merit. For more information visit http://idealeague.org/student-grant/