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A robust parameter estimation method for estimating disease burden of respiratory viruses
King Pan Chan1, Chit Ming Wong1, Susan S S Chiu2
1School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Plos One
|March 22, 2014
Summary
This study found that Poisson models using Bayesian inference and age-specific positive counts provide reliable estimates for respiratory virus disease burden. This approach improves upon traditional methods for influenza and other viruses.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Poisson models are standard for influenza disease burden but struggle with other respiratory viruses.
- Accurate estimation of disease burden for multiple respiratory viruses is crucial for public health planning.
Purpose of the Study:
- To identify the optimal Poisson modeling approach for estimating excess hospitalization rates due to multiple respiratory viruses.
- To compare different inference methods and virus proxies for improved accuracy.
Main Methods:
- Compared Poisson models with various inference methods (Bayesian vs. likelihood) and virus proxies (e.g., age-specific positive counts).
- Validated models by comparing excess hospitalization estimates with observed laboratory-confirmed pediatric acute respiratory infection hospitalizations.
Main Results:
- Bayesian inference generally yielded more accurate estimates than likelihood estimation, especially for RSV and parainfluenza.
- Age-specific positive counts proved superior to other proxies for influenza, RSV, and parainfluenza.
- The combination of Bayesian inference and age-specific positive counts provided reliable estimates for multiple viruses during pandemic and interpandemic periods.
Conclusions:
- Poisson models employing Bayesian inference and age-specific positive counts are recommended for disease burden studies of multiple respiratory viruses.
- This optimized approach enhances the reliability of disease burden estimates for various respiratory pathogens.
