Poisson model and its generalizations applied to dengue data, Brazil
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/8874 |
Resumo: | Objective: analyze and compare the weekly behavior of dengue cases in the five most populous municipalities in the Pernambucan mesoregions, namely Caruaru, Palmares, Recife, Petrolina and Serra Talhada. Method: the weekly epidemiological records of dengue were used, from 2009 to 2018, made available through the Citizen Information Service (SIC). Probability models were applied, more precisely, the Poisson models and their generalizations. Results: the Negative Binomial model stood out in relation to the Quasi-Poisson Model, reducing the dispersion parameters with more precision due to the nature of the overdispersed data. In addition, the analyzes indicated that precipitation and temperature were significant factors that affected the number of cases in some municipalities. Conclusion: modeling has made it a useful tool for local authorities to plan decision-making and intervention in the most propitious periods of proliferation. |
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Poisson model and its generalizations applied to dengue data, BrazilModelo de Poisson y sus generalizaciones aplicadas a datos de dengue, BrasilModelo de Poisson e suas generalizações aplicadas a dados de dengue, BrasilDENVLinear predictorDisseminationModeling.DENVPredictor linealDiseminaciónModelado.DENVPreditor linearDisseminaçãoModelagem. Objective: analyze and compare the weekly behavior of dengue cases in the five most populous municipalities in the Pernambucan mesoregions, namely Caruaru, Palmares, Recife, Petrolina and Serra Talhada. Method: the weekly epidemiological records of dengue were used, from 2009 to 2018, made available through the Citizen Information Service (SIC). Probability models were applied, more precisely, the Poisson models and their generalizations. Results: the Negative Binomial model stood out in relation to the Quasi-Poisson Model, reducing the dispersion parameters with more precision due to the nature of the overdispersed data. In addition, the analyzes indicated that precipitation and temperature were significant factors that affected the number of cases in some municipalities. Conclusion: modeling has made it a useful tool for local authorities to plan decision-making and intervention in the most propitious periods of proliferation.Objetivo: analizar y comparar el comportamiento semanal de los casos de dengue en los cinco municipios más poblados de las mesorregiones de Pernambucana, a saber, Caruaru, Palmares, Recife, Petrolina y Serra Talhada. Método: se utilizaron los registros epidemiológicos semanales de dengue, de 2009 a 2018, disponibles a través del Servicio de Información Ciudadana (SIC). Se aplicaron modelos de probabilidad, más precisamente, los modelos de Poisson y sus generalizaciones. Resultados: el modelo Binomial Negativo se destacó en relación al Modelo Quasi-Poisson, reduciendo los parámetros de dispersión con mayor precisión debido a la naturaleza de los datos sobredispersos. Además, los análisis indicaron que la precipitación y la temperatura fueron factores importantes que afectaron el número de casos en algunos municipios. Conclusión: la modelización la ha convertido en una herramienta útil para que las autoridades locales planifiquen la toma de decisiones y la intervención en los períodos más propicios de proliferación.Objetivo: analisar e comparar o comportamento semanal de casos de dengue nos cinco municípios mais populosos das mesorregiões Pernambucanas, a saber Caruaru, Palmares, Recife, Petrolina e Serra Talhada. Método: utilizaram-se os registros epidemiológicos semanais de dengue, no período de 2009 a 2018, disponibilizados através da Serviço de Informação ao Cidadão (SIC). Foram aplicados modelos de probabilidade, mais precisamente, os modelos Poisson e suas generalizações. Resultados: o modelo Binomial Negativo se sobressaiu em relação ao Modelo Quase-Poisson, reduzindo os parâmetros de dispersões com mais precisão devida à natureza dos dados superdispersos. Além disso, as análises indicaram que a precipitação e temperatura foram fatores significativos que afetou no número de casos em alguns municípios. Conclusão: a modelagem permitiu ser uma ferramenta útil para as autoridades locais planejarem tomadas de decisões e intervenção nos períodos mais propício de proliferação.Research, Society and Development2020-10-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/887410.33448/rsd-v9i10.8874Research, Society and Development; Vol. 9 No. 10; e6629108874Research, Society and Development; Vol. 9 Núm. 10; e6629108874Research, Society and Development; v. 9 n. 10; e66291088742525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/8874/8974Copyright (c) 2020 Jucarlos Rufino de Freitas; Marília Gabriela Ferreira de Miranda Oliveira; Moacyr Cunha Filho; Frank Sinatra Gomes da Silva; Josimar Mendes de Vasconceloshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessFreitas, Jucarlos Rufino deOliveira, Marília Gabriela Ferreira de Miranda Cunha Filho, MoacyrSilva, Frank Sinatra Gomes da Vasconcelos, Josimar Mendes de2020-10-31T12:03:23Zoai:ojs.pkp.sfu.ca:article/8874Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:31:15.040017Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Poisson model and its generalizations applied to dengue data, Brazil Modelo de Poisson y sus generalizaciones aplicadas a datos de dengue, Brasil Modelo de Poisson e suas generalizações aplicadas a dados de dengue, Brasil |
title |
Poisson model and its generalizations applied to dengue data, Brazil |
spellingShingle |
Poisson model and its generalizations applied to dengue data, Brazil Freitas, Jucarlos Rufino de DENV Linear predictor Dissemination Modeling. DENV Predictor lineal Diseminación Modelado. DENV Preditor linear Disseminação Modelagem. |
title_short |
Poisson model and its generalizations applied to dengue data, Brazil |
title_full |
Poisson model and its generalizations applied to dengue data, Brazil |
title_fullStr |
Poisson model and its generalizations applied to dengue data, Brazil |
title_full_unstemmed |
Poisson model and its generalizations applied to dengue data, Brazil |
title_sort |
Poisson model and its generalizations applied to dengue data, Brazil |
author |
Freitas, Jucarlos Rufino de |
author_facet |
Freitas, Jucarlos Rufino de Oliveira, Marília Gabriela Ferreira de Miranda Cunha Filho, Moacyr Silva, Frank Sinatra Gomes da Vasconcelos, Josimar Mendes de |
author_role |
author |
author2 |
Oliveira, Marília Gabriela Ferreira de Miranda Cunha Filho, Moacyr Silva, Frank Sinatra Gomes da Vasconcelos, Josimar Mendes de |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Freitas, Jucarlos Rufino de Oliveira, Marília Gabriela Ferreira de Miranda Cunha Filho, Moacyr Silva, Frank Sinatra Gomes da Vasconcelos, Josimar Mendes de |
dc.subject.por.fl_str_mv |
DENV Linear predictor Dissemination Modeling. DENV Predictor lineal Diseminación Modelado. DENV Preditor linear Disseminação Modelagem. |
topic |
DENV Linear predictor Dissemination Modeling. DENV Predictor lineal Diseminación Modelado. DENV Preditor linear Disseminação Modelagem. |
description |
Objective: analyze and compare the weekly behavior of dengue cases in the five most populous municipalities in the Pernambucan mesoregions, namely Caruaru, Palmares, Recife, Petrolina and Serra Talhada. Method: the weekly epidemiological records of dengue were used, from 2009 to 2018, made available through the Citizen Information Service (SIC). Probability models were applied, more precisely, the Poisson models and their generalizations. Results: the Negative Binomial model stood out in relation to the Quasi-Poisson Model, reducing the dispersion parameters with more precision due to the nature of the overdispersed data. In addition, the analyzes indicated that precipitation and temperature were significant factors that affected the number of cases in some municipalities. Conclusion: modeling has made it a useful tool for local authorities to plan decision-making and intervention in the most propitious periods of proliferation. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-12 |
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/8874 10.33448/rsd-v9i10.8874 |
url |
https://rsdjournal.org/index.php/rsd/article/view/8874 |
identifier_str_mv |
10.33448/rsd-v9i10.8874 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/8874/8974 |
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. 10; e6629108874 Research, Society and Development; Vol. 9 Núm. 10; e6629108874 Research, Society and Development; v. 9 n. 10; e6629108874 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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1797052660784824320 |