Van Boeckel Thomas P, Prosser Diann, Franceschini Gianluca, Biradar Chandra, Wint William, Robinson Tim, Gilbert Marius
Biological Control and Spatial Ecology, Université Libre de Bruxelles CP160/12, Av FD Roosevelt 50, B1050, Brussels, Belgium.
Agric Ecosyst Environ. 2011 May 1;141(3-4):373-380. doi: 10.1016/j.agee.2011.04.013.
Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk factor associated with disease presence. Despite their importance in HPAI epidemiology, their large-scale distribution in monsoon Asia is poorly understood. In this study, we created a spatial database of domestic duck census data in Asia and used it to train statistical distribution models for domestic duck distributions at a spatial resolution of 1 km. The method was based on a modelling framework used by the Food and Agriculture Organisation to produce the Gridded Livestock of the World (GLW) database, and relies on stratified regression models between domestic duck densities and a set of agro-ecological explanatory variables. We evaluated different ways of stratifying the analysis and of combining the prediction to optimize the goodness of fit of the predictions. We found that domestic duck density could be predicted with reasonable accuracy (mean RMSE and correlation coefficient between log-transformed observed and predicted densities being 0.58 and 0.80, respectively), using a stratification based on livestock production systems. We tested the use of artificially degraded data on duck distributions in Thailand and Vietnam as training data, and compared the modelled outputs with the original high-resolution data. This showed, for these two countries at least, that these approaches could be used to accurately disaggregate provincial level (administrative level 1) statistical data to provide high resolution model distributions.
家鸭被认为是高致病性禽流感(HPAI)的重要宿主,多项地理空间研究表明,家鸭已被确定为与疾病存在相关的重要风险因素。尽管家鸭在HPAI流行病学中很重要,但人们对其在亚洲季风区的大规模分布了解甚少。在本研究中,我们创建了一个亚洲家鸭普查数据的空间数据库,并使用它来训练空间分辨率为1公里的家鸭分布统计模型。该方法基于联合国粮食及农业组织用于生成《世界网格化牲畜》(GLW)数据库的建模框架,并依赖于家鸭密度与一组农业生态解释变量之间的分层回归模型。我们评估了分层分析和组合预测的不同方法,以优化预测的拟合优度。我们发现,使用基于畜牧生产系统的分层方法,可以以合理的准确度预测家鸭密度(对数转换后的观测密度与预测密度之间的平均均方根误差和相关系数分别为0.58和0.80)。我们测试了将泰国和越南家鸭分布的人工降解数据用作训练数据,并将建模输出与原始高分辨率数据进行比较。这表明,至少对于这两个国家而言,这些方法可用于准确分解省级(一级行政区)统计数据,以提供高分辨率模型分布。