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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-04-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://doi.org/10.1371/journal.pntd.0011239 |
| _version_ | 1827952367022112768 |
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| author | Alessandra Jacomelli Teles Bianca Conrad Bohm Suellen Caroline Matos Silva Nádia Campos Pereira Bruhn Fábio Raphael Pascoti Bruhn |
| author_facet | Alessandra Jacomelli Teles Bianca Conrad Bohm Suellen Caroline Matos Silva Nádia Campos Pereira Bruhn Fábio Raphael Pascoti Bruhn |
| author_sort | Alessandra Jacomelli Teles |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T13:56:15Z |
| format | Article |
| id | doaj.art-fa83c59ae902433bb4635c1620688136 |
| institution | Directory Open Access Journal |
| issn | 1935-2727 1935-2735 |
| language | English |
| last_indexed | 2024-04-09T13:56:15Z |
| publishDate | 2023-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj.art-fa83c59ae902433bb4635c16206881362023-05-08T05:32:37ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352023-04-01174e001123910.1371/journal.pntd.0011239Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis.Alessandra Jacomelli TelesBianca Conrad BohmSuellen Caroline Matos SilvaNádia Campos Pereira BruhnFábio Raphael Pascoti BruhnAlthough 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.https://doi.org/10.1371/journal.pntd.0011239 |
| spellingShingle | Alessandra Jacomelli Teles Bianca Conrad Bohm Suellen Caroline Matos Silva Nádia Campos Pereira Bruhn Fábio Raphael Pascoti Bruhn Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis. PLoS Neglected Tropical Diseases |
| 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 |
| url | https://doi.org/10.1371/journal.pntd.0011239 |
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