Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss-Newton algorithm

DOI10.1515/jiip-2016-0053OpenAlexW2586634205MaRDI QIDQ2406552

Gerardo Chowell-Puente, M. Sheppard, Linda deCamp, Alexandra B. Smirnova, Seyed M. Moghadas

Publication date: 5 October 2017

Published in: Journal of Inverse and Ill-Posed Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jiip-2016-0053

zbMATH Keywords

parameter estimationepidemiologyiteratively regularized Gauss-Newton algorithm

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Linear operators and ill-posed problems, regularization (47A52)

Related Items (2)

On iteratively regularized predictor–corrector algorithm for parameter identification ^* ⋮ On stable parameter estimation and short-term forecasting with quantified uncertainty with application to COVID-19 transmission

Uses Software

• Matlab

Cites Work

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