Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario. Academic Article uri icon

Overview

abstract

  • Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and deterministic models. Accounting for these departures via stochastic model terms requires full specification of the probabilistic dynamics, and computationally demanding estimation methods. Here, we demonstrate the utility of an alternative approach, generalized profiling, which provides robustness to violations of a deterministic model without needing to specify a complete probabilistic model. We introduce novel means for estimating the robustness parameters and for statistical inference in this framework. The methods are applied to a model for pre-vaccination measles incidence in Ontario, and we demonstrate the statistical validity of our inference through extensive simulation. The results confirm that school term versus summer drives seasonality of transmission, but we find no effects of short school breaks and the estimated basic reproductive ratio (0) greatly exceeds previous estimates. The approach applies naturally to any system for which candidate differential equations are available, and avoids many challenges that have limited Monte Carlo inference for state-space models.

publication date

  • November 17, 2010

Research

keywords

  • Data Interpretation, Statistical
  • Measles
  • Measles virus
  • Models, Immunological

Identity

PubMed Central ID

  • PMC3104327

Scopus Document Identifier

  • 79958052495

Digital Object Identifier (DOI)

  • 10.1126/science.1177373

PubMed ID

  • 21084339

Additional Document Info

volume

  • 8

issue

  • 60