Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis
PLOS Neglected Tropical Diseases·
- DOI
- 10.1371/journal.pntd.0011239
- PMID
- —
- PMCID
- —
- OpenAlex
- W4365482859
- Study type
- Ecological study
- Publisher
- Public Library of Science (PLoS)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The predictive model can serve as a tool for epidemiological analysis and healthcare service planning. The spatial and temporal clustering findings underscore the need for intersectoral surveillance and community control policies addressing municipal disparities in Brazil.
Structured evidence summary
Research question
The study examined spatial and temporal patterns of leptospirosis cases in South Brazil to identify transmission trends, high-risk areas, and to develop a predictive model for disease incidence.
Study design
Ecological study analyzing leptospirosis cases across 497 municipalities in Rio Grande do Sul, Brazil, from 2007 to 2019. Time series analysis used generalized additive models and seasonal autoregressive integrated moving average (SARIMA) models, with spatial analysis applying hotspot density techniques.
Population and setting
The study covered all 497 municipalities in the state of Rio Grande do Sul, South Brazil, focusing on the Centro Oriental Rio Grandense and metropolitan Porto Alegre mesoregions where the highest incidence was recorded.
Main findings
Highest incidence occurred in the Centro Oriental Rio Grandense and metropolitan Porto Alegre mesoregions, identified as high-risk clusters. Temporal peaks were observed in 2011, 2014, and 2019. The SARIMA model predicted declining incidence in early 2020 followed by an increase in the latter half of the year.
Public-health relevance
The predictive model can serve as a tool for epidemiological analysis and healthcare service planning. The spatial and temporal clustering findings underscore the need for intersectoral surveillance and community control policies addressing municipal disparities in Brazil.
Important limitations
This summary is limited to the supplied single-article abstract and metadata. The original paper is required for decision-grade interpretation, including assessment of model validation, data quality, generalizability, and methodological constraints.
GIDS interpretation
This peer-reviewed ecological study provides methods for identifying geographic and temporal patterns of leptospirosis in endemic settings. The disease classification, geographic metadata, and surveillance-relevant topics facilitate discovery in literature searches related to zoonotic disease monitoring and spatial epidemiology in Brazil.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 8 auditable classifier relationships to diseases, places, topics, and study design.