Statistical Science

Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach

Carles Bretó

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Abstract

Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed-form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood maximization, promoting the study of novel data-motivated mechanistic models over the last decade. Reviewing these models is the focus of this paper. In particular, we highlight statistical aspects of these models like overdispersion, which is key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration.

Article information

Source
Statist. Sci., Volume 33, Number 1 (2018), 57-69.

Dates
First available in Project Euclid: 2 February 2018

Permanent link to this document
https://projecteuclid.org/euclid.ss/1517562025

Digital Object Identifier
doi:10.1214/17-STS636

Mathematical Reviews number (MathSciNet)
MR3757504

Keywords
Maximum likelihood iterated filtering particle filter compartment model Lévy-driven stochastic differential equation continuous-time Markov chain environmental stochasticity

Citation

Bretó, Carles. Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach. Statist. Sci. 33 (2018), no. 1, 57--69. doi:10.1214/17-STS636. https://projecteuclid.org/euclid.ss/1517562025


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Supplemental materials

  • Supplement to “Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach”. An illustration of how to apply IF2 for an infectious disease model.
  • Supplement to “Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach”. Source code of supplement.