A nonlinear programming approach for estimation of transmission parameters in childhood infectious disease using a continuous time model.

TitleA nonlinear programming approach for estimation of transmission parameters in childhood infectious disease using a continuous time model.
Publication TypeJournal Article
Year of Publication2012
AuthorsWord DP, Cummings DAT, Burke DS, Iamsirithaworn S, Laird CD
JournalJ R Soc Interface
Volume9
Issue73
Pagination1983-97
Date Published2012 Aug 7
ISSN1742-5662
KeywordsAdolescent, Child, Child, Preschool, Communicable Diseases, Computer Simulation, Female, Humans, Infant, Male, Measles, Models, Biological, Nonlinear Dynamics, Programming Languages, Seasons
Abstract

Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal-dual interior-point solver. The technique is demonstrated using measles case data from three different locations having different school holiday schedules, and our estimates of the seasonality of the transmission parameter show strong correlation to school term holidays. Our approach gives dramatic efficiency gains, showing a 40-400-fold reduction in solution time over other published methods. While our approach has an increased susceptibility to bias over techniques that integrate over the entire unknown state-space, a detailed simulation study shows no evidence of bias. Furthermore, the computational efficiency of our approach allows for investigation of a large model space compared with more computationally intensive approaches.

DOI10.1098/rsif.2011.0829
Alternate JournalJ R Soc Interface
PubMed ID22337634
PubMed Central IDPMC3385750
Grant List5 R01 GM 090204 / GM / NIGMS NIH HHS / United States
R01 GM090204 / GM / NIGMS NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
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