Dynamics identification and forecasting of COVID-19 by switching Kalman filters

DOI10.1007/S00466-020-01911-4zbMath1469.92133OpenAlexW3082589926WikidataQ99239815 ScholiaQ99239815MaRDI QIDQ2221741

X. Zeng, Roger G. Ghanem

Publication date: 2 February 2021

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

Full work available at URL: https://doi.org/10.1007/s00466-020-01911-4

zbMATH Keywords

forecastingCOVID-19dynamics learningswitching Kalman filter

Mathematics Subject Classification ID

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

Related Items (1)

COVID-19 dynamics across the US: a deep learning study of human mobility and social behavior

Uses Software

• GitHub

Cites Work

• Unnamed Item
• Bayesian forecasting and dynamic models
• Time series: theory and methods.
• Analysis and forecast of COVID-19 spreading in China, Italy and France
• Particle filters and Bayesian inference in financial econometrics
• Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India
• Dynamic Linear Models with R

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