A primer on model selection using the Akaike Information Criterion

• Published in: Infectious disease modelling Vol. 5; pp. 111 - 128
• Main Author: Portet, Stéphanie
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01-01-2020; KeAi Publishing; KeAi Communications Co., Ltd
• Subjects: Akaike information criterion; Collection of models; Confronting Infectious Disease Models with Public Health Data; Edited by Prof. Michael Li, Prof. Julien Arino, Prof. Junling Ma, Prof. Zen Jin; Model calibration; Model selection; Akaike information criterion; Model calibration; Collection of models; Model selection
• ISSN: 2468-0427; 2468-2152; 2468-0427
• DOI: 10.1016/j.idm.2019.12.010

A powerful investigative tool in biology is to consider not a single mathematical model but a collection of models designed to explore different working hypotheses and select the best model in that collection. In these lecture notes, the usual workflow of the use of mathematical models to investigate a biological problem is described and the use of a collection of model is motivated. Models depend on parameters that must be estimated using observations; and when a collection of models is considered, the best model has then to be identified based on available observations. Hence, model calibration and selection, which are intrinsically linked, are essential steps of the workflow. Here, some procedures for model calibration and a criterion, the Akaike Information Criterion, of model selection based on experimental data are described. Rough derivation, practical technique of computation and use of this criterion are detailed.