Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study
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Data de Publicação: | 2020 |
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Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/1322 |
Resumo: | Objective. To evaluate predictive power of different time-series models of malaria cases in the state of Amapá, Brazil, in the period 1997-2016. Methods. This is an ecological study of time series with malaria cases registered in the state of Amapá. Ten 3 deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results. The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. The prediction of future cases of malaria in the horizons of 6 and 12 months in advance was possible. Conclusion. It is recommended the use of the ARIMA model to predict future scenarios and to anticipate planning in state health services in the Amazon Region. |
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Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological studyEvaluación de modelos de predicción para la aparición de la malaria en el estado del Amapá, Brasil, 1997-2016: un estudio ecológicoAvaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológicoEstudos de Séries TemporaisMaláriaTécnicas de Apoio para a DecisãoMonitoramento EpidemiológicoPrevisõesTime Series StudiesMalariaDecision Support TechniquesEpidemiological MonitoringForecastingObjective. To evaluate predictive power of different time-series models of malaria cases in the state of Amapá, Brazil, in the period 1997-2016. Methods. This is an ecological study of time series with malaria cases registered in the state of Amapá. Ten 3 deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results. The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. The prediction of future cases of malaria in the horizons of 6 and 12 months in advance was possible. Conclusion. It is recommended the use of the ARIMA model to predict future scenarios and to anticipate planning in state health services in the Amazon Region.Objetivo. Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos. Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados. O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão. Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-10-21info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/132210.1590/SciELOPreprints.1322porhttps://preprints.scielo.org/index.php/scielo/article/view/1322/2074Copyright (c) 2020 Marcos Venicius Malveira de Lima, Gabriel Zorello Laportahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessLima, Marcos Venicius Malveira de Laporta, Gabriel Zorello reponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-10-21T10:39:12Zoai:ops.preprints.scielo.org:preprint/1322Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-10-21T10:39:12SciELO Preprints - SciELOfalse |
dc.title.none.fl_str_mv |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study Evaluación de modelos de predicción para la aparición de la malaria en el estado del Amapá, Brasil, 1997-2016: un estudio ecológico Avaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológico |
title |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
spellingShingle |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study Lima, Marcos Venicius Malveira de Estudos de Séries Temporais Malária Técnicas de Apoio para a Decisão Monitoramento Epidemiológico Previsões Time Series Studies Malaria Decision Support Techniques Epidemiological Monitoring Forecasting |
title_short |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
title_full |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
title_fullStr |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
title_full_unstemmed |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
title_sort |
Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study |
author |
Lima, Marcos Venicius Malveira de |
author_facet |
Lima, Marcos Venicius Malveira de Laporta, Gabriel Zorello |
author_role |
author |
author2 |
Laporta, Gabriel Zorello |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Lima, Marcos Venicius Malveira de Laporta, Gabriel Zorello |
dc.subject.por.fl_str_mv |
Estudos de Séries Temporais Malária Técnicas de Apoio para a Decisão Monitoramento Epidemiológico Previsões Time Series Studies Malaria Decision Support Techniques Epidemiological Monitoring Forecasting |
topic |
Estudos de Séries Temporais Malária Técnicas de Apoio para a Decisão Monitoramento Epidemiológico Previsões Time Series Studies Malaria Decision Support Techniques Epidemiological Monitoring Forecasting |
description |
Objective. To evaluate predictive power of different time-series models of malaria cases in the state of Amapá, Brazil, in the period 1997-2016. Methods. This is an ecological study of time series with malaria cases registered in the state of Amapá. Ten 3 deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results. The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. The prediction of future cases of malaria in the horizons of 6 and 12 months in advance was possible. Conclusion. It is recommended the use of the ARIMA model to predict future scenarios and to anticipate planning in state health services in the Amazon Region. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-21 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/1322 10.1590/SciELOPreprints.1322 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/1322 |
identifier_str_mv |
10.1590/SciELOPreprints.1322 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/1322/2074 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Marcos Venicius Malveira de Lima, Gabriel Zorello Laporta https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Marcos Venicius Malveira de Lima, Gabriel Zorello Laporta 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 |
SciELO Preprints SciELO Preprints SciELO Preprints |
publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO |
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SCI |
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SciELO Preprints |
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SciELO Preprints - SciELO |
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scielo.submission@scielo.org |
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