Bayir Tuba, Gürcan İsmayil Safa
Department of Biometrics, Faculty of Veterinary Medicine, Fırat University, Elazığ, Türkiye, Turkey.
Department of Biostatistics, Faculty of Veterinary Medicine, Ankara University, Ankara, Türkiye, Turkey.
Trop Anim Health Prod. 2024 Sep 27;56(8):290. doi: 10.1007/s11250-024-04180-y.
Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.
小反刍兽疫（PPR）是一种具有重要经济影响的高度严重的跨界疾病，主要发生在绵羊和山羊等小反刍动物中。本研究的目的是确定土耳其小反刍兽疫（PPR）的风险概率和时空聚集情况。本研究利用基于地理信息系统（GIS）的空间分析方法，对2017年至2019年土耳其PPR的发生情况进行了调查。在这些日期之间，共确定了337起疫情和18467例病例。疫情爆发数量最多的地区是安纳托利亚中部地区。研究确定，在土耳其，PPR在绵羊中的发病情况比山羊更为严重。在本研究中，使用了34个环境变量（19个生物气候变量、12个降水变量、海拔和小牲畜密度变量），通过最大熵建模（Maxent）来探究环境对PPR疫情爆发的影响。利用时空置换模型计算的回顾性时空扫描数据，确定了土耳其PPR的聚集情况。利用PPR疫情数据与环境变量相结合，创建了一个PPR预测模型。确定了19个显著（p < 0.001）的时空聚集区。研究发现，海拔、绵羊密度、6月降水量和最暖季节平均温度等变量对模型有重要贡献，PPR疫情爆发可能与这些变量密切相关。在本研究中，对土耳其的PPR在时空和环境因素方面进行了显著特征分析。在此背景下，疾病模式和所得结果将有助于政策制定者对该疾病进行预防和控制。
