Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa

Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans, is one of the most important viral zoonoses in Africa. The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries, namely, Kenya, Tanzania, Uganda and Ethiopia, and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania. Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review. A numerical weight was calculated for each risk factor using an analytical hierarchy process. The corresponding geographic data were collected, standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis. Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of RVF suitability in East Africa. This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge.裂谷热（Rift Valley fever, RVF）是一种经蚊媒传播的病毒性人畜共患病，可感染反刍动物与人类，是非洲地区最为重要的病毒性人畜共患病之一。本研究旨在开发一种基于地理知识的方法，对肯尼亚、坦桑尼亚、乌干达及埃塞俄比亚四个东非国家内适宜裂谷热扩增与传播的区域开展空间制图，并利用肯尼亚与坦桑尼亚的家畜暴发疫情数据评估模型的预测精度。研究通过文献综述明确了与裂谷热扩增及传播相关的风险因子及其相对重要性，并采用层次分析法（Analytic Hierarchy Process, AHP）为各风险因子计算数值权重。随后收集相关地理数据，经标准化处理后基于加权线性组合（Weighted Linear Combination, WLC）进行整合，最终生成裂谷热传播适宜性分布图。本研究利用1998年至2012年间肯尼亚和坦桑尼亚上报的家畜裂谷热暴发点位数据，结合受试者工作特征曲线（Receiver Operating Characteristic curve, ROC）分析，对所得分布图的预测精度进行了评估。研究结果证实，基于地理信息系统（Geographic Information System, GIS）的多准则评价（Multi-criteria Evaluation, MCE）方法能够有效整合现有科学知识，并精准绘制东非地区裂谷热适宜性的空间异质性分布（曲线下面积（Area Under the Curve, AUC）=0.786；95%置信区间（Confidence Interval, CI）[0.730–0.842]）。该方法可为使用者提供简便易行的地图更新途径，可根据数据可得性或科学知识的进一步发展对地图进行更新完善。