System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

DOI10.1007/s00466-020-01894-2zbMath1465.92132arXiv2007.00865OpenAlexW3048608718WikidataQ113326877 ScholiaQ113326877MaRDI QIDQ2221739

Gregory H. Teichert, Krishna Garikipati, Zhongxin Wang, M. Carrasco-Teja, Xiaoxuan Zhang

Publication date: 2 February 2021

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.00865

zbMATH Keywords

optimizationneural networksinverse problemscompartmental modelsepidemiology

Mathematics Subject Classification ID

Epidemiology (92D30)

Related Items

Identification of time delays in COVID-19 data ⋮ Optimal control of a reaction–diffusion model related to the spread of COVID-19 ⋮ Well‐posedness for a diffusion–reaction compartmental model simulating the spread of COVID‐19 ⋮ COVID-19 dynamics across the US: a deep learning study of human mobility and social behavior ⋮ Adaptive mesh refinement and coarsening for diffusion-reaction epidemiological models ⋮ Accounting for super-spreader events and algebraic decay in SIR models

Uses Software

• SciPy
• Python
• dolfin-adjoint

Cites Work

• Numerical modelling of an SIR epidemic model with diffusion
• Multilayer feedforward networks are universal approximators
• Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
• Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions
• Variational system identification of the partial differential equations governing the physics of pattern-formation: inference under varying fidelity and noise
• Localized outbreaks in an S-I-R model with diffusion
• The Mathematics of Infectious Diseases