Mintiens K, Laevens H, Dewulf J, Boelaert F, Verloo D, Koenen F
Coordination Centre for Veterinary Diagnostics, Veterinary and Agrochemical Research Centre, Groeselenberg 99, 1180 Brussels, Belgium.
Prev Vet Med. 2003 Jul 30;60(1):27-36. doi: 10.1016/s0167-5877(03)00080-1.
Risk factors associated with the occurrence of "neighbourhood infections" [Epidemiology of classical swine fever. In: Truszczynski, M. (Ed.), Proceedings of the Workshop on Diagnostic Procedures and Measures to Control Classical Swine Fever in Domestic Pigs and the European Wild Boar. Pulaway, Poland, pp. 119-130] during classical swine fever (CSF) outbreaks were examined based on information collected during a CSF-epidemic, which occurred in the East Flanders Province of Belgium in 1994. The only risk factor that was associated with the occurrence of "neighbourhood infections" was a kernel estimation of the intensity of neighbouring herds (P=0.055) [Interactive spatial data analysis. Pearson Education Limited, Harlow, Essex], i.e. the higher the kernel estimation, the higher the risk for the occurrence of neighbourhood infections. In a second part of the study, the likelihood for the occurrence of neighbourhood infections within an area with a 1 km radius was predicted for every Belgian pig herd, assuming that the herd was infected with CSF-virus. For the prediction of these likelihoods, the model resulting from the risk assessment was used. Finally, the predicted likelihoods were transformed into a raster map after applying a smoothing technique. As a result, different areas in Belgium of higher or lower risk for CSF-virus spread through "neighbourhood infections" could be identified on the map. The areas in Belgium where CSF-outbreaks including "neighbourhood infections" occurred in the past decades were all predicted by the model to be of high risk.
基于1994年在比利时东佛兰德省发生的经典猪瘟（CSF）疫情期间收集的信息，对经典猪瘟疫情期间与“周边感染”（[经典猪瘟流行病学。见：Truszczynski, M.（编辑），家猪和欧洲野猪经典猪瘟诊断程序及控制措施研讨会论文集。波兰普拉瓦，第119 - 130页]）发生相关的风险因素进行了研究。与“周边感染”发生相关的唯一风险因素是对相邻猪群感染强度的核估计（P = 0.055）[交互式空间数据分析。培生教育出版有限公司，埃塞克斯郡哈洛]，即核估计值越高，发生周边感染的风险越高。在研究的第二部分，假设每个比利时猪群感染了CSF病毒，预测了半径为1公里区域内发生周边感染的可能性。为了预测这些可能性，使用了风险评估得出的模型。最后，在应用平滑技术后，将预测的可能性转换为栅格地图。结果，可以在地图上识别出比利时不同地区通过“周边感染”传播CSF病毒的风险高低。该模型预测出过去几十年中比利时发生包括“周边感染”在内的CSF疫情的所有地区都具有高风险。
