Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/11138 |
Resumo: | In the last five years, the number of Dengue cases has been growing sharply in the city of Garanhuns. The objective of this study was to determine an analysis of the time series of Dengue cases in the medium-sized municipality, associated with climatic factors that contribute to the occurrence of this disease with forecasts, thus facilitating better control and prevention. Methodology: The autoregressive model of seasonal moving averages with exogenous variables (SARIMAX) was applied, which is a linear regression model that involves a process of the SARIMA model. In addition to the graphical analysis of the decomposition of time series, the Dickey-Fuller test was used to assess the stationarity of the series. Considering the seasonal behavior and the non-stationarity of the time series, the adjusted model had as parameters the SARIMA model (p, d, q) (P, D, Q), applying the Akaike Information Criterion (AIC) to select the best model, using the software R. Result: Considering the seasonal component and the non-stationarity of the time series, the model with the best adjustment was SARIMA (0,1,3) (0.1.1), a significance level of 5% (p-value = 0, 01). The SARIMAX model (0, 1, 3) (0,1,1) plus the effect of temperature and humidity were adequate to report the incidence of Dengue. In the correlation, the increase in the temperature component was greater than the humidity in the number of Dengue cases. |
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Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019Análisis temporal del dengue asociado a factores climáticos en Garanhuns, Pernambuco, Brasil, de 2010 a 2019Análise temporal da dengue associada a fatores climáticos em Garanhuns, Pernambuco, Brasil, de 2010 a 2019IncidenciaDengueEstacionalidadEpidemiaPrevención.IncidênciaDengueSazonalidadeEpidemiaPrevenção.IncidenceDengueSeasonalityEpidemicPrevention.In the last five years, the number of Dengue cases has been growing sharply in the city of Garanhuns. The objective of this study was to determine an analysis of the time series of Dengue cases in the medium-sized municipality, associated with climatic factors that contribute to the occurrence of this disease with forecasts, thus facilitating better control and prevention. Methodology: The autoregressive model of seasonal moving averages with exogenous variables (SARIMAX) was applied, which is a linear regression model that involves a process of the SARIMA model. In addition to the graphical analysis of the decomposition of time series, the Dickey-Fuller test was used to assess the stationarity of the series. Considering the seasonal behavior and the non-stationarity of the time series, the adjusted model had as parameters the SARIMA model (p, d, q) (P, D, Q), applying the Akaike Information Criterion (AIC) to select the best model, using the software R. Result: Considering the seasonal component and the non-stationarity of the time series, the model with the best adjustment was SARIMA (0,1,3) (0.1.1), a significance level of 5% (p-value = 0, 01). The SARIMAX model (0, 1, 3) (0,1,1) plus the effect of temperature and humidity were adequate to report the incidence of Dengue. In the correlation, the increase in the temperature component was greater than the humidity in the number of Dengue cases.En los últimos cinco años, el número de casos de dengue ha aumentado considerablemente en la ciudad de Garanhuns (Pernambuco). El objetivo de este estudio fue determinar un análisis de series de tiempo de casos de Dengue en el municipio de tamaño mediano, asociados a factores climáticos que contribuyen a la ocurrencia de esta enfermedad con pronósticos, facilitando así un mejor control y prevención de la contaminación. Metodología: Se aplicó el modelo autorregresivo de promedios móviles estacionales con variables exógenas (SARIMAX), un modelo de regresión lineal que involucra un proceso del modelo SARIMA. Además del análisis gráfico de la descomposición de series temporales, se utilizó la prueba de Dickey-Fuller para evaluar la estacionariedad de la serie. Considerando el comportamiento estacional y la no estacionariedad de la serie temporal, el modelo ajustado tuvo como parámetro el modelo SARIMA (p, d, q) (P, D, Q), aplicando el criterio de Información de Akaike (AIC) para seleccionar el mejor modelo, utilizando el software R Resultado: Considerando el componente estacional y la no estacionariedad de la serie temporal, el modelo con mejor ajuste fue SARIMA (0.1.3) (0.1.1), nivel de significancia del 5% ( valor -p = 0,01). El modelo SARIMAX (0, 1, 3) (0, 1, 1) más el efecto de la temperatura y la humedad fueron adecuados para reportar la incidencia de Dengue. En la correlación, el incremento en el componente de temperatura fue mayor que la humedad en el número de casos de Dengue.Nos últimos cinco anos, o número de casos de Dengue vem crescendo acentuadamente na cidade de Garanhuns (Pernambuco). O objetivo deste estudo foi determinar uma análise de séries temporais de casos de Dengue no município de médio porte, associadas a fatores climáticos que contribuem para a ocorrência dessa doença com previsões, facilitando assim um melhor controle e prevenção de contaminações. Metodologia: Foi aplicado o modelo autorregressivo de médias móveis sazonais com variáveis exógenas (SARIMAX) - modelo de regressão linear que envolve um processo do modelo SARIMA. Além da análise gráfica da decomposição das séries temporais, foi utilizado o teste de Dickey-Fuller para avaliar a estacionariedade das séries. Considerando o comportamento sazonal e a não estacionariedade das séries temporais, o modelo ajustado teve como parâmetro o modelo SARIMA (p, d, q) (P, D, Q), sendo aplicado o critério Akaike Information (AIC) para a seleção do melhor modelo, utilizando o software R Resultado: Considerando o componente sazonal e a não estacionariedade das séries temporais, o modelo com melhor ajuste foi o SARIMA (0,1,3) (0,1,1), nível de significância de 5% (p-valor = 0,01). O modelo SARIMAX (0, 1, 3) (0, 1, 1) mais o efeito da temperatura e da umidade foram adequados para relatar a incidência de Dengue. Na correlação, o incremento do componente temperatura foi maior do que a umidade no número de casos de Dengue.Research, Society and Development2020-12-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1113810.33448/rsd-v9i12.11138Research, Society and Development; Vol. 9 No. 12; e22891211138Research, Society and Development; Vol. 9 Núm. 12; e22891211138Research, Society and Development; v. 9 n. 12; e228912111382525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/11138/9860Copyright (c) 2020 Petrúcio Luiz Lins de Morais; Priscila Mayrelle Silva Castanha; Gabriela Isabel Limoeiro Alves Nascimento; Ulisses Ramos Montarroyoshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMorais, Petrúcio Luiz Lins deCastanha, Priscila Mayrelle SilvaNascimento, Gabriela Isabel Limoeiro AlvesMontarroyos, Ulisses Ramos2020-12-30T23:32:22Zoai:ojs.pkp.sfu.ca:article/11138Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:33:02.772679Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 Análisis temporal del dengue asociado a factores climáticos en Garanhuns, Pernambuco, Brasil, de 2010 a 2019 Análise temporal da dengue associada a fatores climáticos em Garanhuns, Pernambuco, Brasil, de 2010 a 2019 |
title |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
spellingShingle |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 Morais, Petrúcio Luiz Lins de Incidencia Dengue Estacionalidad Epidemia Prevención. Incidência Dengue Sazonalidade Epidemia Prevenção. Incidence Dengue Seasonality Epidemic Prevention. |
title_short |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
title_full |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
title_fullStr |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
title_full_unstemmed |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
title_sort |
Temporal analysis of dengue associated with climatic factors in Garanhuns, Pernambuco, Brazil, from 2010 to 2019 |
author |
Morais, Petrúcio Luiz Lins de |
author_facet |
Morais, Petrúcio Luiz Lins de Castanha, Priscila Mayrelle Silva Nascimento, Gabriela Isabel Limoeiro Alves Montarroyos, Ulisses Ramos |
author_role |
author |
author2 |
Castanha, Priscila Mayrelle Silva Nascimento, Gabriela Isabel Limoeiro Alves Montarroyos, Ulisses Ramos |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Morais, Petrúcio Luiz Lins de Castanha, Priscila Mayrelle Silva Nascimento, Gabriela Isabel Limoeiro Alves Montarroyos, Ulisses Ramos |
dc.subject.por.fl_str_mv |
Incidencia Dengue Estacionalidad Epidemia Prevención. Incidência Dengue Sazonalidade Epidemia Prevenção. Incidence Dengue Seasonality Epidemic Prevention. |
topic |
Incidencia Dengue Estacionalidad Epidemia Prevención. Incidência Dengue Sazonalidade Epidemia Prevenção. Incidence Dengue Seasonality Epidemic Prevention. |
description |
In the last five years, the number of Dengue cases has been growing sharply in the city of Garanhuns. The objective of this study was to determine an analysis of the time series of Dengue cases in the medium-sized municipality, associated with climatic factors that contribute to the occurrence of this disease with forecasts, thus facilitating better control and prevention. Methodology: The autoregressive model of seasonal moving averages with exogenous variables (SARIMAX) was applied, which is a linear regression model that involves a process of the SARIMA model. In addition to the graphical analysis of the decomposition of time series, the Dickey-Fuller test was used to assess the stationarity of the series. Considering the seasonal behavior and the non-stationarity of the time series, the adjusted model had as parameters the SARIMA model (p, d, q) (P, D, Q), applying the Akaike Information Criterion (AIC) to select the best model, using the software R. Result: Considering the seasonal component and the non-stationarity of the time series, the model with the best adjustment was SARIMA (0,1,3) (0.1.1), a significance level of 5% (p-value = 0, 01). The SARIMAX model (0, 1, 3) (0,1,1) plus the effect of temperature and humidity were adequate to report the incidence of Dengue. In the correlation, the increase in the temperature component was greater than the humidity in the number of Dengue cases. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11138 10.33448/rsd-v9i12.11138 |
url |
https://rsdjournal.org/index.php/rsd/article/view/11138 |
identifier_str_mv |
10.33448/rsd-v9i12.11138 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11138/9860 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 9 No. 12; e22891211138 Research, Society and Development; Vol. 9 Núm. 12; e22891211138 Research, Society and Development; v. 9 n. 12; e22891211138 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052666650558464 |