ArticleScientific reports2022
Spatial-temporal patterns and risk factors for human leptospirosis in Thailand, 2012-2018.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Machine learning reveals temperature as a key predictor of dengue risk across Thailand's provinces: A 20-year analysis.PLoS neglected tropical diseases · 2026Article
- Spatio-temporal ensemble machine learning forecasts of animal leptospirosis in enzootic Indian States using integrated environmental predictors.Veterinary research communications · 2026Article
- Pathogenic Leptospira species identified in dogs and cats during neutering in Thailand.PLoS neglected tropical diseases · 2026Article
- Leptospirosis in Central Romania: A 17-Year Single-Center Cohort Study of Hospitalized Adults.Microorganisms · 2026Article
- Development of a Mobile App (MyLepto App) to Improve Knowledge, Attitude, and Practice Regarding Leptospirosis Among Wet Market Workers in Selangor, Malaysia: Protocol for a Quasi-Experimental Study.JMIR research protocols · 2026Article
- Spatiotemporal patterns and climate influences on leptospirosis in Sri Lanka from 2009 to 2024.BMC infectious diseases · 2026Article
- Leptospirosis in Southeast Asia: Investigating Seroprevalence, Transmission Patterns, and Diagnostic Challenges.Tropical medicine and infectious disease · 2026Review
- Policy Options for Prevention of Leptospirosis Epidemics: A Scoping Review.Iranian journal of public health · 2025Review
- The Role of Hydrometeorological Factors in Leptospirosis Transmission in Central Java, Indonesia.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2025Article
- Endemicity, Clinical Features, Risk Factors, and the Potential for Severe Infection inTropical medicine and infectious disease · 2025Article
- Unraveling the drivers of leptospirosis risk in Thailand using machine learning.PLoS neglected tropical diseases · 2025Article
- Leptospirosis-Improving Healthcare Outcomes for a Neglected Tropical Disease.Open forum infectious diseases · 2025Review
- Article
- Spatial patterns of laboratory-confirmed leptospirosis in north-eastern Peninsular Malaysia, 2016-2023.Epidemiology and health · 2025Article
- Geospatial Analysis of Abiotic and Biotic Conditions Associated with Leptospirosis in the Klaten Regency, Central Java, Indonesia.Tropical medicine and infectious disease · 2024Article
- Epidemiological Features of Leptospirosis and Identification ofInfectious disease reports · 2024Article
- Analysing the outbreaks of leptospirosis after floods in Kerala, India.International journal of health geographics · 2024Article
- Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.BMC public health · 2023Article
- The combination of RPA-CRISPR/Cas12a and Leptospira IgM RDT enhances the early detection of leptospirosis.PLoS neglected tropical diseases · 2023Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran's I index was calculated for incidences per 100,000 population at the province level during 2012-2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran's I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice crop arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.
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