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

Autor: Teles AJ; Secretaria Municipal de Saúde de Herval, Herval, Rio Grande do Sul, Brasil., Bohm BC; Universidade Federal de Pelotas, Programa de Pós Graduação em Veterinária, Capão do Leão, Rio Grande do Sul, Brasil., Silva SCM; Universidade Federal de Pelotas, Programa de Pós Graduação em Veterinária, Capão do Leão, Rio Grande do Sul, Brasil., Bruhn NCP; Centro de Integração do Mercosul, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brasil., Bruhn FRP; Departamento de Veterinária Preventiva, Universidade Federal de Pelotas, Capão do Leão, Rio Grande do Sul, Brasil.
Jazyk: angličtina
Zdroj: PLoS neglected tropical diseases [PLoS Negl Trop Dis] 2023 Apr 14; Vol. 17 (4), pp. e0011239. Date of Electronic Publication: 2023 Apr 14 (Print Publication: 2023).
DOI: 10.1371/journal.pntd.0011239
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Teles et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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