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dc.contributor.authorLega, Joceline
dc.contributor.authorBrown, Heidi E.
dc.date.accessioned2017-03-11T00:59:51Z
dc.date.available2017-03-11T00:59:51Z
dc.date.issued2016-12
dc.identifier.citationData-driven outbreak forecasting with a simple nonlinear growth model 2016, 17:19 Epidemicsen
dc.identifier.issn17554365
dc.identifier.pmid27770752
dc.identifier.doi10.1016/j.epidem.2016.10.002
dc.identifier.urihttp://hdl.handle.net/10150/622814
dc.description.abstractRecent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. (C) 2016 The Authors. Published by Elsevier B.V.
dc.description.sponsorshipNational Institute of Allergy and Infectious Diseases of the National Institutes of Health [K01AI101224]en
dc.language.isoenen
dc.publisherELSEVIER SCIENCE BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1755436516300329en
dc.rightsCopyright © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInfectious disease outbreaksen
dc.subjectMathematical modelen
dc.subjectSurge capacityen
dc.subjectChikungunya virus infectionen
dc.titleData-driven outbreak forecasting with a simple nonlinear growth modelen
dc.typeArticleen
dc.contributor.departmentUniv Arizonaen
dc.identifier.journalEpidemicsen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
html.description.abstractRecent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. (C) 2016 The Authors. Published by Elsevier B.V.


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Copyright © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).