Clements Archie C A, Pfeiffer Dirk U, Martin Vincent, Otte M Joachim
Epidemiology Division, Department of Veterinary Clinical Sciences, Royal Veterinary College, University of London, Hatfield, Hertfordshire, United Kingdom.
Prev Vet Med. 2007 Nov 15;82(1-2):72-82. doi: 10.1016/j.prevetmed.2007.05.006. Epub 2007 Jun 14.
Rift Valley fever (RVF) epidemics have serious consequences for human and animal health and the livestock trade. Recent epidemics have occurred in previously unaffected regions, increasing concerns that the geographical range of RVF will continue to expand. We conducted an extensive, systematic review of the literature to obtain serological data for RVF in Africa, collected between 1970 and 2000 from human, livestock and wild ungulate populations. Aims were to calculate sub-national estimates of RVF infection prevalence and to define areas where no information was available. We presented the data (aggregated at the first administrative level of countries) using a geographical information system. Data from 71 publications were used to build a spatially explicit Bayesian logistic-regression model, with spatial and non-spatial random effects, allowing us to identify clusters of high and low RVF seroprevalence, and fixed effects that described the disparate nature of the survey subjects and methods. Significant high-prevalence clusters encompassed areas that had experienced epidemics during the late 20th century and significant low-prevalence clusters were located in contiguous areas of Western and Central Africa.
裂谷热（RVF）疫情对人类和动物健康以及牲畜贸易有着严重影响。近期疫情发生在以前未受影响的地区，这增加了人们对裂谷热地理范围将继续扩大的担忧。我们对文献进行了广泛、系统的综述，以获取1970年至2000年间在非洲从人类、牲畜和野生有蹄类动物群体中收集的裂谷热血清学数据。目的是计算裂谷热感染率的国家以下层面估计值，并确定没有可用信息的地区。我们使用地理信息系统展示了这些数据（在国家的第一行政级别进行汇总）。来自71份出版物的数据被用于构建一个具有空间和非空间随机效应的空间明确贝叶斯逻辑回归模型，这使我们能够识别裂谷热血清阳性率高和低的集群，以及描述调查对象和方法不同性质的固定效应。显著的高流行率集群包括20世纪后期经历过疫情的地区，显著的低流行率集群位于西非和中非的相邻地区。
