Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India.
Department of Geoinformatics, Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India.
Vet Res Commun. 2022 Sep;46(3):967-978. doi: 10.1007/s11259-022-09902-x. Epub 2022 Feb 23.
Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.
蓝舌病(BT)对全球牲畜种群构成持续威胁。更好地了解风险因素将能够更准确地预测高风险事件的地点和时间。在特定地理区域内对一段时间内的疾病爆发进行绘图将确定疾病发生的空间分布。在印度安得拉邦,使用基于地理信息系统(GIS)的方法来分析蓝舌病流行与时空模式之间的关系,时间范围为 2000 年至 2015 年。进行了自相关(ACF)、偏自相关(PACF)和互相关(CCF)分析,以发现 BT 流行与环境因素和牲畜种群之间的自相关性及其依存关系。还研究了不同月份滞后的气候或遥感变量(包括风速、温度、降雨量、相对湿度、归一化差异植被指数(NDVI)、归一化差异水指数(NDWI)、地表温度(LST))与蓝舌病的关联。BT 流行及其滞后的 ACF 和 PACF 显示出与一个月滞后(r=0.41)的显著正自相关。环境变量与 BT 流行之间的互相关表明,降雨量、相对湿度、归一化差异水指数(NDWI)和归一化差异植被指数(NDVI)的 0、1 和 2 个月滞后均具有显著正相关。空间自相关分析估计了单变量全局 Moran's I 值为 0.21。同时,2000 年的局部 Moran's I 值(r=0.32)显示出高度的空间自相关。空间自相关分析表明,绵羊中的 BT 流行在附近地区的爆发之间具有相当大的空间关联,在进行任何预测或疾病预测时都需要考虑其他风险因素。