Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling.
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–outpu...
| Publicado en: | Systems Research & Behavioral Science Vol. 37; no. 6; pp. 936 - 959 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Nov2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=148147860&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 148147860 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10927026 2SN jtl: Systems Research & Behavioral Science issn: 10927026 maglogo: Y pubinfo: dt: Nov2020 vid: 37 iid: 6 pid: 480 pub: Wiley-Blackwell artinfo: ui: 148147860 10.1002/sres.2763 ppf: 936 ppct: 23 formats: tig: atl: Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling. aug: au: Edali, Mert Yücel, Gönenç affil: Department of Industrial Engineering, Bogazici University, Istanbul, Turkey Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey Chicago Center for HIV Elimination, University of Chicago, Chicago IL,, USA Department of Medicine, University of Chicago, Chicago IL,, USA su: Epidemics Immunization Influenza epidemiology Statistical models Random forest algorithms sug: subj: Epidemics Immunization Influenza epidemiology Statistical models Random forest algorithms keyword: adaptive sequential sampling FluTE individual‐based modelling metamodeling rule extraction adaptive sequential sampling FluTE individual‐based modelling metamodeling rule extraction ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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