Open AccessInterior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models
Author(s) -
Daniel P. Word,
James K. Young,
Derek A. T. Cummings,
Sopon Iamsirithaworn,
Carl D. Laird
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0074208
Subject(s) - infectious disease (medical specialty) , measles , computer science , multiplicative function , econometrics , discrete time and continuous time , statistics , mathematical optimization , mathematics , disease , biology , medicine , pathology , immunology , vaccination , mathematical analysis
Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities. These models include multiplicative measurement noise and incorporate seasonality into multiple model parameters. Our results show that nearly identical patterns are estimated even when assuming seasonality in different model parameters, and that these patterns show strong correlation to school term holidays across very different social settings and holiday schedules. We show that interior-point methods provide a fast and flexible approach to parameterizing models that can be an alternative to more computationally intensive methods.