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Contemporary statistical inference for infectious disease models using Stan.

Anastasia Chatzilena¹, Edwin van Leeuwen², Oliver Ratmann³

¹Department of Economics, Athens University of Economics and Business, Athens, Greece.

|October 9, 2019

Summary

This summary is machine-generated.

This study explores advanced statistical methods for understanding infectious disease dynamics. Hamiltonian Monte Carlo and variational inference are computationally feasible for epidemic modeling, offering a speed-efficiency trade-off for real-time analysis.

Keywords:

Automatic differentiation variational inference Epidemic models Hamiltonian Monte Carlo No-U-turn sampler Stan

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Area of Science:

Epidemiology
Computational Statistics
Mathematical Biology

Background:

Infectious disease dynamics modeling is crucial for public health.
Statistical inference methods are essential for understanding disease spread.
Recent advances in statistical computation offer new possibilities for complex models.

Purpose of the Study:

To apply recent statistical advances to infectious disease dynamics inference.
To evaluate Hamiltonian Monte Carlo and variational inference for fitting epidemic models.
To assess the computational feasibility and performance of these methods on real-world data.

Main Methods:

Fitting a class of epidemic models using Hamiltonian Monte Carlo.
Utilizing variational inference for parameter estimation in epidemic models.
Implementation within the Stan software environment for accessibility.

Main Results:

Both Hamiltonian Monte Carlo and variational inference were found to be computationally feasible.
A trade-off exists between statistical efficiency and computational speed for the methods.
Computational speed is particularly relevant for real-time infectious disease outbreak analysis.

Conclusions:

Advanced statistical inference methods are applicable to infectious disease dynamics.
The choice between Hamiltonian Monte Carlo and variational inference depends on specific application needs (e.g., real-time analysis).
Stan software provides a practical platform for implementing these advanced statistical techniques in epidemiology.