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Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling
Author(s) -
Edali Mert,
Yücel Gönenç
Publication year - 2020
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2763
Subject(s) - metamodeling , computer science , random forest , set (abstract data type) , block (permutation group theory) , sampling (signal processing) , machine learning , artificial intelligence , data mining , mathematics , software engineering , geometry , filter (signal processing) , computer vision , programming language
Abstract This study proposes a three‐step procedure for the analysis of input–response relationships of dynamic models, which enables the analyst to develop a better understanding about the dynamics of the system. The main building block of the procedure is a random forest metamodel capturing the input–output relationships. We utilize an active learning approach as the second step to improve the accuracy of the metamodel. In the last step, we develop a novel way to present the information captured by the metamodel as a set of intelligible IF–THEN rules. For illustration, we use the FluTE model, which is an individual‐based influenza epidemic model. We observe that the number of daily applicable vaccines determines the success of an intervention strategy the most. Another critical observation is that when the daily available vaccines are constrained, nonpharmaceutical strategies should be incorporated to reduce the extent of the outbreak.