Machine learning in predicting severe acute respiratory infection outbreaks
Autor(a) principal: | |
---|---|
Data de Publicação: | 2024 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cadernos de Saúde Pública |
Texto Completo: | https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471 |
Resumo: | Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series. |
id |
FIOCRUZ-5_1c1106ecbb8c7a54447ddda744bbc334 |
---|---|
oai_identifier_str |
oai:ojs.teste-cadernos.ensp.fiocruz.br:article/8471 |
network_acronym_str |
FIOCRUZ-5 |
network_name_str |
Cadernos de Saúde Pública |
repository_id_str |
|
spelling |
Machine learning in predicting severe acute respiratory infection outbreaksAprendizaje automático en la predicción de brotes de síndrome respiratorio agudo graveAprendizado de máquina na previsão de surtos de síndrome respiratória aguda graveSíndrome Respiratória Aguda Grave; Aprendizado de Máquina; Modelos Computacionais; Vigilância Epidemiológica; Redes Neurais (Computação)Síndrome Respiratorio Agudo Grave; Aprendizaje Automático; Modelos de Ordenador; Vigilancia Epidemiológica; Redes Neurales (Computación)Severe Acute Respiratory Infection; Machine Learning; Computer Models; Epidemiologic Surveillance; Neural Networks (Computer)Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.Reports in Public HealthCadernos de Saúde Pública2024-01-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/xmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471Reports in Public Health; Vol. 40 No. 1 (2024): JanuaryCadernos de Saúde Pública; v. 40 n. 1 (2024): Janeiro1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471/18882https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471/18883Copyright (c) 2023 Cadernos de Saúde Públicainfo:eu-repo/semantics/openAccessDuarte da Silva, AmauriFerreira da Costa Gomes, MarceloSchäffer Gregianini, TatianaGaray Martins, LetíciaBeatriz Gorini da Veiga, Ana2024-01-02T17:04:31Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/8471Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:09:38.976138Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true |
dc.title.none.fl_str_mv |
Machine learning in predicting severe acute respiratory infection outbreaks Aprendizaje automático en la predicción de brotes de síndrome respiratorio agudo grave Aprendizado de máquina na previsão de surtos de síndrome respiratória aguda grave |
title |
Machine learning in predicting severe acute respiratory infection outbreaks |
spellingShingle |
Machine learning in predicting severe acute respiratory infection outbreaks Duarte da Silva, Amauri Síndrome Respiratória Aguda Grave; Aprendizado de Máquina; Modelos Computacionais; Vigilância Epidemiológica; Redes Neurais (Computação) Síndrome Respiratorio Agudo Grave; Aprendizaje Automático; Modelos de Ordenador; Vigilancia Epidemiológica; Redes Neurales (Computación) Severe Acute Respiratory Infection; Machine Learning; Computer Models; Epidemiologic Surveillance; Neural Networks (Computer) |
title_short |
Machine learning in predicting severe acute respiratory infection outbreaks |
title_full |
Machine learning in predicting severe acute respiratory infection outbreaks |
title_fullStr |
Machine learning in predicting severe acute respiratory infection outbreaks |
title_full_unstemmed |
Machine learning in predicting severe acute respiratory infection outbreaks |
title_sort |
Machine learning in predicting severe acute respiratory infection outbreaks |
author |
Duarte da Silva, Amauri |
author_facet |
Duarte da Silva, Amauri Ferreira da Costa Gomes, Marcelo Schäffer Gregianini, Tatiana Garay Martins, Letícia Beatriz Gorini da Veiga, Ana |
author_role |
author |
author2 |
Ferreira da Costa Gomes, Marcelo Schäffer Gregianini, Tatiana Garay Martins, Letícia Beatriz Gorini da Veiga, Ana |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Duarte da Silva, Amauri Ferreira da Costa Gomes, Marcelo Schäffer Gregianini, Tatiana Garay Martins, Letícia Beatriz Gorini da Veiga, Ana |
dc.subject.por.fl_str_mv |
Síndrome Respiratória Aguda Grave; Aprendizado de Máquina; Modelos Computacionais; Vigilância Epidemiológica; Redes Neurais (Computação) Síndrome Respiratorio Agudo Grave; Aprendizaje Automático; Modelos de Ordenador; Vigilancia Epidemiológica; Redes Neurales (Computación) Severe Acute Respiratory Infection; Machine Learning; Computer Models; Epidemiologic Surveillance; Neural Networks (Computer) |
topic |
Síndrome Respiratória Aguda Grave; Aprendizado de Máquina; Modelos Computacionais; Vigilância Epidemiológica; Redes Neurais (Computação) Síndrome Respiratorio Agudo Grave; Aprendizaje Automático; Modelos de Ordenador; Vigilancia Epidemiológica; Redes Neurales (Computación) Severe Acute Respiratory Infection; Machine Learning; Computer Models; Epidemiologic Surveillance; Neural Networks (Computer) |
description |
Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-02 |
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://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471 |
url |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471/18882 https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/8471/18883 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Cadernos de Saúde Pública info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Cadernos de Saúde Pública |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/xml application/pdf |
dc.publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
dc.source.none.fl_str_mv |
Reports in Public Health; Vol. 40 No. 1 (2024): January Cadernos de Saúde Pública; v. 40 n. 1 (2024): Janeiro 1678-4464 0102-311X reponame:Cadernos de Saúde Pública instname:Fundação Oswaldo Cruz (FIOCRUZ) instacron:FIOCRUZ |
instname_str |
Fundação Oswaldo Cruz (FIOCRUZ) |
instacron_str |
FIOCRUZ |
institution |
FIOCRUZ |
reponame_str |
Cadernos de Saúde Pública |
collection |
Cadernos de Saúde Pública |
repository.name.fl_str_mv |
Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ) |
repository.mail.fl_str_mv |
cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br |
_version_ |
1798943399724711936 |