Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario
Giles Hooker1, Stephen P Ellner, Laura De Vargas Roditi
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA. gjh27@cornell.edu
Journal of the Royal Society, Interface
|November 19, 2010
Summary
Generalized profiling offers a robust method for estimating parameters in infectious disease models, improving accuracy without complex probabilistic modeling. This approach enhances understanding of disease transmission and epidemic forecasting.
Area of Science:
- Epidemiology
- Mathematical Biology
- Statistical Inference
Background:
- Parameter estimation is crucial for understanding infectious disease dynamics, forecasting epidemics, and designing control strategies.
- Traditional differential equation models face numerical challenges and discrepancies with observational data.
- Stochastic models require full probabilistic specification and computationally intensive methods.
Purpose of the Study:
- To introduce and demonstrate the utility of generalized profiling for parameter estimation in infectious disease models.
- To provide a robust approach that accommodates violations of deterministic model assumptions without full probabilistic specification.
- To develop novel methods for estimating robustness parameters and performing statistical inference within this framework.
Main Methods:
- Application of generalized profiling to estimate parameters for a measles incidence model in pre-vaccination Ontario.
- Development of new methods for estimating robustness parameters and conducting statistical inference.
- Validation of the statistical validity of the inference through extensive simulations.
Main Results:
- Generalized profiling demonstrated robustness to deterministic model violations.
- Seasonal transmission patterns were confirmed, driven by school terms versus summer.
- No significant effect of short school breaks on transmission was detected.
- Estimated basic reproductive ratio (R0) significantly exceeded previous estimates.
Conclusions:
- Generalized profiling provides a computationally efficient and robust alternative for parameter estimation in infectious disease modeling.
- The method is applicable to various systems with candidate differential equations, overcoming limitations of traditional state-space models.
- Findings highlight the importance of school terms in driving measles seasonality and provide updated estimates for the basic reproductive ratio.
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