Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter

DOI10.1016/j.cam.2022.114772zbMath1500.92119arXiv2110.14892OpenAlexW3210619740MaRDI QIDQ2088868

Yanyan Li

Publication date: 6 October 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.14892

zbMATH Keywords

data assimilationensemble Kalman filterCOVID-19extended SEIR model

Mathematics Subject Classification ID

Epidemiology (92D30) Filtering in stochastic control theory (93E11)

Related Items (1)

Predicting the behavior of sparsely-sampled systems across neurobiology and epidemiology

Uses Software

• EnKF
• GitHub

Cites Work

• Mathematical epidemiology.
• Variational data assimilation with epidemic models
• An introduction to mathematical modeling of infectious diseases
• Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission
• An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation
• Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
• Analyzing the effects of observation function selection in ensemble Kalman filtering for epidemic models
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• The Mathematics of Infectious Diseases
• Data Assimilation

This page was built for publication: Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter