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dc.creatorCosta, Anna Cecília Trolesi Reis Borges-
dc.creatorPereira, Carine Rodrigues-
dc.creatorSáfadi, Thelma-
dc.creatorHeinemann, Marcos Bryan-
dc.creatorDorneles, Elaine Maria Seles-
dc.date.accessioned2022-02-08T19:51:18Z-
dc.date.available2022-02-08T19:51:18Z-
dc.date.issued2021-06-
dc.identifier.citationCOSTA, A. C. T. R. B. et al. Climate influence the human leptospirosis cases in Brazil, 2007–2019: a time series analysis. Transactions of The Royal Society of Tropical Medicine and Hygiene, Oxford, v. 116, n. 2, p. 124–132, Feb. 2022. DOI: https://doi.org/10.1093/trstmh/trab092.pt_BR
dc.identifier.urihttps://doi.org/10.1093/trstmh/trab092pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49220-
dc.description.abstractBackground: Human leptospirosis is responsible for great losses and deaths, especially in developing countries, which can be mitigated by knowing the correct health indicators and climate influence on the disease. Methods: Leptospirosis cases and deaths, population and precipitation were recovered from different databases (2007–2019). Annual incidence, mortality and case fatality rates (CFRs) of human leptospirosis and average precipitation were calculated for Brazil and its regions. Time series analysis using an moving average with external variable (ARMAX) model was used to analyse the monthly contribution and precipitation influence over leptospirosis cases for each Brazilian region and for the whole country. A forecast model to predict cases for 2020 was created for Brazil. Results: Human leptospirosis exhibited heterogeneous distribution among Brazilian regions, with most cases occurring during the rainy season and precipitation influenced the disease occurrence in all regions but the South. The forecast model predicted 3276.99 cases for 2020 (mean absolute percentage error 14.680 and root mean square error 53.013). Considering the annual average for the period, the leptospirosis incidence was 1913 cases per 100 000 inhabitants, mortality was 0.168 deaths per 100 000 inhabitants and the CFR was 8.83%. Conclusions: The models built can be useful for planning leptospirosis surveillance and control actions for the whole country and its regions and, together with the health indicators, revealed no uniform epidemiological situation of leptospirosis in Brazil.pt_BR
dc.languageenpt_BR
dc.publisherOxford University Presspt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceTransactions of The Royal Society of Tropical Medicine and Hygienept_BR
dc.subjectARMAXpt_BR
dc.subjectEpidemiologypt_BR
dc.subjectModellingpt_BR
dc.subjectTime series analysispt_BR
dc.subjectZoonosispt_BR
dc.subjectLeptospirose humanapt_BR
dc.subjectEpidemiologiapt_BR
dc.subjectModelagempt_BR
dc.subjectAnálise de séries temporaispt_BR
dc.subjectZoonosept_BR
dc.titleClimate influence the human leptospirosis cases in Brazil, 2007–2019: a time series analysispt_BR
dc.typeArtigopt_BR
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