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dc.contributor.authorSiegenfeld, Alexander F.
dc.contributor.authorKollepara, Pratyush K.
dc.contributor.authorBar-Yam, Yaneer
dc.date.accessioned2023-02-14T12:58:52Z
dc.date.available2023-02-14T12:58:52Z
dc.date.issued2022-10-29
dc.identifier.urihttps://hdl.handle.net/1721.1/148027
dc.description.abstractCompartmental epidemic models have been widely used for predicting the course of epidemics, from estimating the basic reproduction number to guiding intervention policies. Studies commonly acknowledge these models’ assumptions but less often justify their validity in the specific context in which they are being used. Our purpose is not to argue for specific alternatives or modifications to compartmental models, but rather to show how assumptions can constrain model outcomes to a narrow portion of the wide landscape of potential epidemic behaviors. This concrete examination of well-known models also serves to illustrate general principles of modeling that can be applied in other contexts.en_US
dc.publisherHindawien_US
dc.relation.isversionofhttp://dx.doi.org/10.1155/2022/3007864en_US
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceHindawien_US
dc.titleModeling Complex Systems: A Case Study of Compartmental Models in Epidemiologyen_US
dc.typeArticleen_US
dc.identifier.citationAlexander F. Siegenfeld, Pratyush K. Kollepara, and Yaneer Bar-Yam, “Modeling Complex Systems: A Case Study of Compartmental Models in Epidemiology,” Complexity, vol. 2022, Article ID 3007864, 12 pages, 2022. doi:10.1155/2022/3007864en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-02-12T08:00:21Z
dc.language.rfc3066en
dc.rights.holderCopyright © 2022 Alexander F. Siegenfeld et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dspace.date.submission2023-02-12T08:00:20Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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