ArticlePLoS neglected tropical diseases2018
Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji.
Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review.JMIR medical informatics · 2025Pooled it
- A review of drivers of emerging infectious diseases within the wildlife-human-domestic animal interface in the West Pacific Tropics.One health (Amsterdam, Netherlands) · 2026Review
- Synergistic effects of multifactor interactions on the transmission of Echinococcus spp. on the Qinghai-Tibet Plateau.PLoS neglected tropical diseases · 2026Article
- Avian influenza risk mapping in India using machine learning.Frontiers in veterinary science · 2026Article
- Leptospirosis in Campinas, Brazil: The interplay between drainage, impermeable areas, and social vulnerability.PLoS neglected tropical diseases · 2025Observational
- Geospatial analysis of leptospirosis clusters and risk factors in two provinces of the Dominican Republic.PLoS neglected tropical diseases · 2025Article
- Changing epidemiology of leptospirosis in China from 1955 to 2022.Infectious diseases of poverty · 2025Article
- Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.PLoS neglected tropical diseases · 2025Article
- Article
- Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study.PLOS global public health · 2023Article
- Article
- Bayesian Network Analysis of Lymphatic Filariasis Serology from Myanmar Shows Benefit of Adding Antibody Testing to Post-MDA Surveillance.Tropical medicine and infectious disease · 2022Article
- Assessment of Environmental Impacts on Health: Examples from the Pacific Basin.Annals of global health · 2022Review
- Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste.International journal for parasitology · 2021Article
- Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction.Risk analysis : an official publication of the Society for Risk Analysis · 2021Article
- Community venue exposure risk estimator for the COVID-19 pandemic.Health & place · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 7 institutions in 5 countries.
Funding
Abstract
introductionLeptospirosis is a zoonotic disease responsible for over 1 million severe cases and 60,000 deaths annually. The wide range of animal hosts and complex environmental drivers of transmission make targeted interventions challenging, particularly when restricted to regression-based analyses which have limited ability to deal with complexity. In Fiji, important environmental and socio-demographic factors include living in rural areas, poverty, and livestock exposure. This study aims to examine drivers of transmission under different scenarios of environmental and livestock exposures.
methodsSpatial Bayesian networks (SBN) were used to analyse the influence of livestock and poverty on the risk of leptospirosis infection in urban compared to rural areas. The SBN models used a combination of spatially-explicit field data from previous work and publically available census information. Predictive risk maps were produced for overall risk, and for scenarios related to poverty, livestock, and urban/rural setting.
resultsWhile high, rather than low, commercial dairy farm density similarly increased the risk of infection in both urban (12% to 18%) and rural areas (70% to 79%), the presence of pigs in a village had different impact in rural (43% to 84%) compared with urban areas (4% to 24%). Areas with high poverty rates were predicted to have 26.6% and 18.0% higher probability of above average seroprevalence in rural and urban areas, respectively. In urban areas, this represents >300% difference between areas of low and high poverty, compared to 43% difference in rural areas.
conclusionsOur study demonstrates the use of SBN to provide valuable insights into the drivers of leptospirosis transmission under complex scenarios. By estimating the risk of leptospirosis infection under different scenarios, such as urban versus rural areas, these subgroups or areas can be targeted with more precise interventions that focus on the most relevant key drivers of infection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.