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Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis

Although leptospirosis is endemic in most Brazilian regions, South Brazil shows the highest morbidity and mortality rates in the country. The present study aimed to analyze the spatial and temporal dynamics of leptospirosis cases in South Brazil to identify the temporal trends and high-risk areas fo...

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Autores principales: Teles, Alessandra Jacomelli, Bohm, Bianca Conrad, Silva, Suellen Caroline Matos, Bruhn, Nádia Campos Pereira, Bruhn, Fábio Raphael Pascoti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132658/
https://www.ncbi.nlm.nih.gov/pubmed/37058534
http://dx.doi.org/10.1371/journal.pntd.0011239
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author Teles, Alessandra Jacomelli
Bohm, Bianca Conrad
Silva, Suellen Caroline Matos
Bruhn, Nádia Campos Pereira
Bruhn, Fábio Raphael Pascoti
author_facet Teles, Alessandra Jacomelli
Bohm, Bianca Conrad
Silva, Suellen Caroline Matos
Bruhn, Nádia Campos Pereira
Bruhn, Fábio Raphael Pascoti
author_sort Teles, Alessandra Jacomelli
collection PubMed
description Although leptospirosis is endemic in most Brazilian regions, South Brazil shows the highest morbidity and mortality rates in the country. The present study aimed to analyze the spatial and temporal dynamics of leptospirosis cases in South Brazil to identify the temporal trends and high-risk areas for transmission and to propose a model to predict the disease incidence. An ecological study of leptospirosis cases in the 497 municipalities of the state of Rio Grande do Sul, Brazil, was conducted from 2007 to 2019. The spatial distribution of disease incidence in southern Rio Grande do Sul municipalities was evaluated, and a high incidence of the disease was identified using the hotspot density technique. The trend of leptospirosis over the study period was evaluated by time series analyses using a generalized additive model and a seasonal autoregressive integrated moving average model to predict its future incidence. The highest incidence was recorded in the Centro Oriental Rio Grandense and metropolitan of Porto Alegre mesoregions, which were also identified as clusters with a high incidence and high risk of contagion. The analysis of the incidence temporal series identified peaks in the years 2011, 2014, and 2019. The SARIMA model predicted a decline in incidence in the first half of 2020, followed by an increase in the second half. Thus, the developed model proved to be adequate for predicting leptospirosis incidence and can be used as a tool for epidemiological analyses and healthcare services.Temporal and spatial clustering of leptospirosis cases highlights the demand for intersectorial surveillance and community control policies, with a focus on reducing the disparity among municipalities in Brazil.
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spelling pubmed-101326582023-04-27 Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis Teles, Alessandra Jacomelli Bohm, Bianca Conrad Silva, Suellen Caroline Matos Bruhn, Nádia Campos Pereira Bruhn, Fábio Raphael Pascoti PLoS Negl Trop Dis Research Article Although leptospirosis is endemic in most Brazilian regions, South Brazil shows the highest morbidity and mortality rates in the country. The present study aimed to analyze the spatial and temporal dynamics of leptospirosis cases in South Brazil to identify the temporal trends and high-risk areas for transmission and to propose a model to predict the disease incidence. An ecological study of leptospirosis cases in the 497 municipalities of the state of Rio Grande do Sul, Brazil, was conducted from 2007 to 2019. The spatial distribution of disease incidence in southern Rio Grande do Sul municipalities was evaluated, and a high incidence of the disease was identified using the hotspot density technique. The trend of leptospirosis over the study period was evaluated by time series analyses using a generalized additive model and a seasonal autoregressive integrated moving average model to predict its future incidence. The highest incidence was recorded in the Centro Oriental Rio Grandense and metropolitan of Porto Alegre mesoregions, which were also identified as clusters with a high incidence and high risk of contagion. The analysis of the incidence temporal series identified peaks in the years 2011, 2014, and 2019. The SARIMA model predicted a decline in incidence in the first half of 2020, followed by an increase in the second half. Thus, the developed model proved to be adequate for predicting leptospirosis incidence and can be used as a tool for epidemiological analyses and healthcare services.Temporal and spatial clustering of leptospirosis cases highlights the demand for intersectorial surveillance and community control policies, with a focus on reducing the disparity among municipalities in Brazil. Public Library of Science 2023-04-14 /pmc/articles/PMC10132658/ /pubmed/37058534 http://dx.doi.org/10.1371/journal.pntd.0011239 Text en © 2023 Teles et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Teles, Alessandra Jacomelli
Bohm, Bianca Conrad
Silva, Suellen Caroline Matos
Bruhn, Nádia Campos Pereira
Bruhn, Fábio Raphael Pascoti
Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title_full Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title_fullStr Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title_full_unstemmed Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title_short Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
title_sort spatial and temporal dynamics of leptospirosis in south brazil: a forecasting and nonlinear regression analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132658/
https://www.ncbi.nlm.nih.gov/pubmed/37058534
http://dx.doi.org/10.1371/journal.pntd.0011239
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