Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA.
Sci Rep. 2023 Aug 19;13(1):13526. doi: 10.1038/s41598-023-40865-4.
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.
口蹄疫(FMD)是一种由核糖核酸(RNA)病毒引起的高度传染性动物疾病,在亚洲、非洲和南美洲造成了巨大的经济损失,且分布不均。虽然口蹄疫的空间分析和建模仍处于早期阶段,但本研究旨在通过研究 2017 年 3 月 21 日至 2018 年 3 月 21 日期间报告的 135 起疫情,在省级层面上确定伊朗口蹄疫发病的社会环境决定因素。我们获得了 46 个潜在的社会环境决定因素,并选择了四个变量,包括人口百分比、1 月降水量、绵羊百分比和山羊百分比,用于空间回归模型来估计空间异质性的变化。在我们的分析中,我们使用了全局模型,即普通最小二乘法(OLS)、空间误差模型(SEM)和空间滞后模型(SLM),以及局部模型,包括地理加权回归(GWR)和多尺度地理加权回归(MGWR)。MGWR 模型的调整 [Formula: see text]最高,达到 90%,优于其他局部和全局模型。使用局部模型来绘制环境决定因素(如绵羊百分比和降水)对口蹄疫发病空间变异性的影响,可以为决策者提供有针对性干预的有用信息。我们的研究结果主张采取多尺度和多学科政策来降低口蹄疫的发病率。