Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.

Murphy, Ryan J; Maclaren, Oliver J; Calabrese, Alivia R; Thomas, Patrick B; Warne, David J; Williams, Elizabeth D; Simpson, Matthew J

Murphy, Ryan J; Maclaren, Oliver J; Calabrese, Alivia R; Thomas, Patrick B; Warne, David J; Williams, Elizabeth D; Simpson, Matthew J.

Affiliation

Murphy RJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Maclaren OJ; Department of Engineering Science, University of Auckland, Auckland, New Zealand.
Calabrese AR; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia.
Thomas PB; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia.
Warne DJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Williams ED; Queensland Bladder Cancer Initiative and School of Biomedical Sciences, Faculty of Health, Queensland University of Technology at Translational Research Institute, Brisbane, Australia.
Simpson MJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.

J R Soc Interface ; 19(197): 20220560, 2022 12.

Article in En | MEDLINE | ID: mdl-36475389

Subject(s)

Population Growth

Key words

identifiability analysis; population dynamics; profile likelihood; uncertainty quantification

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Population Growth Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: J R Soc Interface Year: 2022 Type: Article Affiliation country: Australia

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