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

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Title: Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm.
Authors: Smirnova, Alexandra1 asmirnova@gsu.edu, Chowell-Puente, Gerardo2 gchowell@gsu.edu, deCamp, Linda1 ldecamp1@student.gsu.edu, Moghadas, Seyed3 moghadas@yorku.ca, Jameson Sheppard, Michael1 msheppard6@student.gsu.edu
Source: Journal of Inverse & Ill-Posed Problems. Oct2017, Vol. 25 Issue 5, p653-667. 15p.
Database: Mathematics Source
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Inverse+%26+Ill-Posed+Problems%22">Journal of Inverse & Ill-Posed Problems</searchLink>. Oct2017, Vol. 25 Issue 5, p653-667. 15p.
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      – Type: doi
        Value: 10.1515/jiip-2016-0053
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 653
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      – TitleFull: Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm.
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            NameFull: Smirnova, Alexandra
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            NameFull: Chowell-Puente, Gerardo
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            NameFull: deCamp, Linda
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            NameFull: Jameson Sheppard, Michael
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            – D: 01
              M: 10
              Text: Oct2017
              Type: published
              Y: 2017
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              Value: 25
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