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Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models
Kris V Parag1, Christl A Donnelly1,2
1MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK.
Systematic Biology
|April 26, 2020
Summary
A new method using minimum description length (MDL) optimizes model complexity for estimating epidemic growth (R) and population size (N). This approach provides more reliable insights into population dynamics and epidemic trends.
Area of Science:
- Ecology and Epidemiology
- Phylodynamics
- Information Theory
Background:
- Estimating temporal population changes from phylogenetic or count data is crucial for understanding ecological and epidemiological dynamics.
- Current models like renewal (for R) and skyline (for N) rely on piecewise-constant functions, but misspecification of their complexity (p) leads to unreliable estimates.
- Existing methods for selecting complexity (p) are often heuristic or obscure, hindering trustworthy conclusions.
Purpose of the Study:
- To introduce a transparent, principled, and computable method for selecting the optimal complexity parameter (p) in piecewise models.
- To improve the reliability of estimates for effective reproduction number (R) and effective population size (N) in epidemiological and ecological studies.
- To provide a robust model selection framework that accounts for statistical complexity and parameter interactions.
Main Methods:
- Developed a p-selection method based on the minimum description length (MDL) formalism from information theory.
- Applied the MDL-based method to optimize piecewise-constant functions used in renewal and skyline models.
- Compared the performance of the MDL approach against standard model selection criteria like Akaike and Bayesian information criteria.
Main Results:
- The proposed MDL method provides a computable and interpretable way to optimize model complexity (p).
- This approach ensures that estimates of R and N adapt meaningfully to the available data, outperforming traditional criteria.
- The method reveals underlying statistical similarities between different biological models, including renewal and skyline plots.
Conclusions:
- Rigorous and interpretable model selection is essential for drawing trustworthy conclusions from piecewise models in phylodynamics and epidemiology.
- The MDL-based p-selection method offers a significant advancement for accurate estimation of epidemic dynamics and population history.
- This work facilitates better understanding and forecasting of population changes driven by ecological and epidemiological factors.
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