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Dengue outbreaks: unpredictable incidence time series

Dengue fever is a disease with increasing incidence, now occurring in some regions which were not previously affected. Ribeirão Preto and São Paulo, municipalities in São Paulo state, Brazil, have been highlighted due to the high dengue incidences especially after 2009 and 2013. Therefore, the curre...

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Autores principales: Gabriel, A.F.B., Alencar, A.P., Miraglia, S.G.E.K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518828/
https://www.ncbi.nlm.nih.gov/pubmed/30869035
http://dx.doi.org/10.1017/S0950268819000311
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author Gabriel, A.F.B.
Alencar, A.P.
Miraglia, S.G.E.K.
author_facet Gabriel, A.F.B.
Alencar, A.P.
Miraglia, S.G.E.K.
author_sort Gabriel, A.F.B.
collection PubMed
description Dengue fever is a disease with increasing incidence, now occurring in some regions which were not previously affected. Ribeirão Preto and São Paulo, municipalities in São Paulo state, Brazil, have been highlighted due to the high dengue incidences especially after 2009 and 2013. Therefore, the current study aims to analyse the temporal behaviour of dengue cases in the both municipalities and forecast the number of disease cases in the out-of-sample period, using time series models, especially SARIMA model. We fitted SARIMA models, which satisfactorily meet the dengue incidence data collected in the municipalities of Ribeirão Preto and São Paulo. However, the out-of-sample forecast confidence intervals are very wide and this fact is usually omitted in several papers. Despite the high variability, health services can use these models in order to anticipate disease scenarios, however, one should interpret with prudence since the magnitude of the epidemic may be underestimated.
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spelling pubmed-65188282019-06-04 Dengue outbreaks: unpredictable incidence time series Gabriel, A.F.B. Alencar, A.P. Miraglia, S.G.E.K. Epidemiol Infect Original Paper Dengue fever is a disease with increasing incidence, now occurring in some regions which were not previously affected. Ribeirão Preto and São Paulo, municipalities in São Paulo state, Brazil, have been highlighted due to the high dengue incidences especially after 2009 and 2013. Therefore, the current study aims to analyse the temporal behaviour of dengue cases in the both municipalities and forecast the number of disease cases in the out-of-sample period, using time series models, especially SARIMA model. We fitted SARIMA models, which satisfactorily meet the dengue incidence data collected in the municipalities of Ribeirão Preto and São Paulo. However, the out-of-sample forecast confidence intervals are very wide and this fact is usually omitted in several papers. Despite the high variability, health services can use these models in order to anticipate disease scenarios, however, one should interpret with prudence since the magnitude of the epidemic may be underestimated. Cambridge University Press 2019-03-01 /pmc/articles/PMC6518828/ /pubmed/30869035 http://dx.doi.org/10.1017/S0950268819000311 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Gabriel, A.F.B.
Alencar, A.P.
Miraglia, S.G.E.K.
Dengue outbreaks: unpredictable incidence time series
title Dengue outbreaks: unpredictable incidence time series
title_full Dengue outbreaks: unpredictable incidence time series
title_fullStr Dengue outbreaks: unpredictable incidence time series
title_full_unstemmed Dengue outbreaks: unpredictable incidence time series
title_short Dengue outbreaks: unpredictable incidence time series
title_sort dengue outbreaks: unpredictable incidence time series
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518828/
https://www.ncbi.nlm.nih.gov/pubmed/30869035
http://dx.doi.org/10.1017/S0950268819000311
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