Semantic Scholar Open Access 2020 36 sitasi

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

Zhenlin Wang Xiaoxuan Zhang G. Teichert M. Carrasco-Teja K. Garikipati

Lihat Sumber DOI

Abstrak

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al. in Comput Methods Appl Mech Eng 356:44–74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.

Topik & Kata Kunci

Biology Computer Science Medicine

Penulis (5)

Zhenlin Wang

Xiaoxuan Zhang

G. Teichert

M. Carrasco-Teja

K. Garikipati

Format Sitasi

APA MLA BibTeX

Wang, Z., Zhang, X., Teichert, G., Carrasco-Teja, M., Garikipati, K. (2020). System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19. https://doi.org/10.1007/s00466-020-01894-2

Akses Cepat

Lihat di Sumber doi.org/10.1007/s00466-020-01894-2

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 36×
Sumber Database: Semantic Scholar
DOI: 10.1007/s00466-020-01894-2
Akses: Open Access ✓