Abstract:
:Rigorously calibrating dynamic models with time-series data can pose roadblocks. Oftentimes, the problem is ill-posed and one has to rely on appropriate regularization techniques to ensure stable parameter estimation from which forward projections with quantified uncertainty could be generated. If the inversion procedure is cast as nonlinear least squares constrained by a system of nonlinear differential equations, then the system has to be solved numerically at every step of the iterative process and the corresponding parameter-to-data map cannot be used to evaluate the Fréchet derivative analytically. To address challenges related to both instability and Jacobian approximation, we propose a novel regularized Levenberg-Marquardt algorithm with iterative rank-one updates for computation of the derivative operator. In order to test the efficiency of this scheme, we conduct numerical experiments using a mathematical model of infectious disease transmission and real incidence data of historic measles outbreaks in the UK.
journal_name
Bull Math Bioljournal_title
Bulletin of mathematical biologyauthors
Smirnova A,Sirb B,Chowell Gdoi
10.1007/s11538-019-00650-9subject
Has Abstractpub_date
2019-10-01 00:00:00pages
4210-4232issue
10eissn
0092-8240issn
1522-9602pii
10.1007/s11538-019-00650-9journal_volume
81pub_type
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