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

Journal of the Royal Society, Interface
Daniel P WordCarl D Laird

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 s...Continue Reading

References

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Jun 19, 2009·Journal of the Royal Society, Interface·Daihai HeAaron A King
Nov 19, 2010·Journal of the Royal Society, Interface·Giles HookerDavid J D Earn

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