ArticleScientific reports2023
Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Older adults life expectancy in China: Bayesian mortality estimation and spatially heterogeneous determinants.Frontiers in public health · 2026Article
- From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors.Transboundary and emerging diseases · 2026Article
- Modeling the drivers of mumps incidence in China: a spatial multi-method analysis.Frontiers in public health · 2026Article
- Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.BMC public health · 2025Article
- Spatiotemporal analysis of the re-emerging foot and mouth disease outbreak in Central Java, Indonesia.Open veterinary journal · 2025Article
- A multi-method spatial analysis of dysentery incidence determinants across Chinese provinces.Frontiers in public health · 2025Article
- Subjective social integration and its spatially varying determinants of rural-to-urban migrants among Chinese cities.Scientific reports · 2024Article
- Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.BMC public health · 2023Article
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7 authors.
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Abstract
Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.
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