Data-driven outbreak forecasting with a simple nonlinear growth model
| dc.contributor.author | Lega, Joceline | |
| dc.contributor.author | Brown, Heidi E. | |
| dc.date.accessioned | 2017-03-11T00:59:51Z | |
| dc.date.available | 2017-03-11T00:59:51Z | |
| dc.date.issued | 2016-12 | |
| dc.identifier.citation | Data-driven outbreak forecasting with a simple nonlinear growth model 2016, 17:19 Epidemics | en |
| dc.identifier.issn | 17554365 | |
| dc.identifier.pmid | 27770752 | |
| dc.identifier.doi | 10.1016/j.epidem.2016.10.002 | |
| dc.identifier.uri | http://hdl.handle.net/10150/622814 | |
| dc.description.abstract | Recent 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.sponsorship | National Institute of Allergy and Infectious Diseases of the National Institutes of Health [K01AI101224] | en |
| dc.language.iso | en | en |
| dc.publisher | ELSEVIER SCIENCE BV | en |
| dc.relation.url | http://linkinghub.elsevier.com/retrieve/pii/S1755436516300329 | en |
| dc.rights | 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/). | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Infectious disease outbreaks | en |
| dc.subject | Mathematical model | en |
| dc.subject | Surge capacity | en |
| dc.subject | Chikungunya virus infection | en |
| dc.title | Data-driven outbreak forecasting with a simple nonlinear growth model | en |
| dc.type | Article | en |
| dc.contributor.department | Univ Arizona | en |
| dc.identifier.journal | Epidemics | en |
| dc.description.collectioninformation | This 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.version | Final published version | en |
| html.description.abstract | Recent 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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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/).