Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81271
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dc.contributor.authorZhao, Sen_US
dc.contributor.authorMusa, SSen_US
dc.contributor.authorFu, Hen_US
dc.contributor.authorHe, DHen_US
dc.contributor.authorQin, Jen_US
dc.date.accessioned2019-09-20T00:54:50Z-
dc.date.available2019-09-20T00:54:50Z-
dc.date.issued2019-
dc.identifier.citationParasites & vectors, 12 July 2019, v. 12, 344, p. 1-13en_US
dc.identifier.urihttp://hdl.handle.net/10397/81271-
dc.description.abstractBackground In 2015-2016, Zika virus (ZIKV) caused serious epidemics in Brazil. The key epidemiological parameters and spatial heterogeneity of ZIKV epidemics in different states in Brazil remain unclear. Early prediction of the final epidemic (or outbreak) size for ZIKV outbreaks is crucial for public health decision-making and mitigation planning. We investigated the spatial heterogeneity in the epidemiological features of ZIKV across eight different Brazilian states by using simple non-linear growth models.en_US
dc.description.abstractResults We fitted three different models to the weekly reported ZIKV cases in eight different states and obtained an R-2 larger than 0.995. The estimated average values of basic reproduction numbers from different states varied from 2.07 to 3.41, with a mean of 2.77. The estimated turning points of the epidemics also varied across different states. The estimation of turning points nevertheless is stable and real-time. The forecast of the final epidemic size (attack rate) is reasonably accurate, shortly after the turning point. The knowledge of the epidemic turning point is crucial for accurate real-time projection of the outbreak.en_US
dc.description.abstractConclusions Our simple models fitted the epidemic reasonably well and thus revealed the spatial heterogeneity in the epidemiological features across Brazilian states. The knowledge of the epidemic turning point is crucial for real-time projection of the outbreak size. Our real-time estimation framework is able to yield a reliable prediction of the final epidemic size.en_US
dc.description.sponsorshipDepartment of Applied Mathematicsen_US
dc.description.sponsorshipSchool of Nursingen_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.relation.ispartofParasites & vectorsen_US
dc.rights© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rightsThe following publication Zhao, S., Musa, S. S., Fu, H., He, D. H., & Qin, J. (2019). Simple framework for real-time forecast in a data-limited situation: the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example. Parasites & Vectors, 12, 344, 1-13 is available at https://dx.doi.org/10.1186/s13071-019-3602-9en_US
dc.subjectZika virusen_US
dc.subjectBrazilen_US
dc.subjectModeling analysisen_US
dc.subjectReproduction numberen_US
dc.subjectEpidemic sizeen_US
dc.subjectSpatial heterogeneityen_US
dc.titleSimple framework for real-time forecast in a data-limited situation : the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an exampleen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage13-
dc.identifier.volume12-
dc.identifier.doi10.1186/s13071-019-3602-9-
dc.identifier.isiWOS:000475557400003-
dc.identifier.scopus2-s2.0-85068975197-
dc.identifier.pmid31300061-
dc.identifier.eissn1756-3305-
dc.identifier.artn344-
dc.description.validate201909 bcrc-
dc.description.oapublished_final-
Appears in Collections:Journal/Magazine Article
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