期刊
INFECTIOUS DISEASE MODELLING
卷 5, 期 -, 页码 111-128出版社
KEAI PUBLISHING LTD
DOI: 10.1016/j.idm.2019.12.010
关键词
Collection of models; Model calibration; Model selection; Akaike information criterion
资金
- Natural Sciences and Engineering Research Council of Canada [RGOIN-2018-04967]
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. (c) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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