A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

DOI10.3934/mbe.2021381zbMath1501.92197arXiv2106.07919OpenAlexW3196413947MaRDI QIDQ2092173

Yukun Tan, Durward Cator III, Martial Ndeffo-Mbah, Ulisses M. Braga-Neto

Publication date: 2 November 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.07919

zbMATH Keywords

parameter estimationadaptive filteringmaximum likelihoodepidemic modelunscented Kalman filternonlinear stochastic modelSEIRD model

Mathematics Subject Classification ID

Epidemiology (92D30) Estimation and detection in stochastic control theory (93E10) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Cites Work

• Unnamed Item
• Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty
• Unbounded solutions of models for glycolysis
• Identification and estimation of the SEIRD epidemic model for COVID-19
• Forecasting seasonal influenza with a state-space SIR model
• Gaussian filters for nonlinear filtering problems
• A Numerical-Integration Perspective on Gaussian Filters
• Hyperbolic compartmental models for epidemic spread on networks with uncertain data: Application to the emergence of COVID-19 in Italy

This page was built for publication: A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread