Open AccessModeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach
Author(s) -
Carles Bretó
Publication year - 2018
Publication title -
statistical science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.204
H-Index - 108
eISSN - 2168-8745
pISSN - 0883-4237
DOI - 10.1214/17-sts636
Subject(s) - overdispersion , inference , computer science , infectious disease (medical specialty) , expectation–maximization algorithm , context (archaeology) , machine learning , statistical inference , likelihood function , statistical model , maximum likelihood , data science , artificial intelligence , econometrics , estimation theory , mathematics , count data , disease , statistics , algorithm , biology , poisson distribution , medicine , paleontology , pathology
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.