Spatiotemporal patterns and climate influences on leptospirosis in Sri Lanka from 2009 to 2024
BMC Infectious Diseases·
- DOI
- 10.1186/s12879-026-12533-1
- PMID
- —
- PMCID
- —
- OpenAlex
- W7122408599
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The authors reported that climate-sensitive modelling could support integration of meteorological surveillance into leptospirosis early-warning and response systems, particularly for identified hotspot districts. This is the study’s stated implication and does not establish that such systems improve outcomes.
Structured evidence summary
Research question
The study examined how leptospirosis cases varied across space and time in Sri Lanka from 2009 to 2024, and assessed associations between district-level incidence and climatic variables.
Study design
This was a retrospective district-level ecological analysis using monthly case and climate data. Generalized additive distributional modelling assessed associations and spatial heterogeneity, while Moran’s I assessed spatial clustering and autocorrelation.
Population and setting
The analysis covered confirmed leptospirosis cases reported monthly across districts in Sri Lanka between 2009 and 2024. Climate measures were obtained from the NASA POWER satellite dataset.
Main findings
The study reported 81,629 confirmed cases. Relative humidity and maximum temperature had immediate negative associations with incidence, while humidity, mean temperature, and rainfall had positive associations at selected one- to three-month lags; spatial clustering was reported in Ratnapura, Galle, Matara, and Hambantota, with Colombo identified as a spatial outlier.
Public-health relevance
The authors reported that climate-sensitive modelling could support integration of meteorological surveillance into leptospirosis early-warning and response systems, particularly for identified hotspot districts. This is the study’s stated implication and does not establish that such systems improve outcomes.
Important limitations
The supplied abstract does not state explicit study limitations. The evidence summary is therefore limited to the supplied single-article abstract and metadata; the original paper is required for decision-grade assessment of data quality, model assumptions, confounding, exposure measurement, and generalizability.
GIDS interpretation
The article is discoverable as evidence concerning leptospirosis, climate and environment, surveillance, transmission dynamics, and Sri Lanka. It reports modeled historical district-level associations and spatial patterns from 2009–2024; it does not confirm or characterize a current surveillance signal.
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 7 auditable classifier relationships to diseases, places, topics, and study design.