Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium.
PLoS One. 2012;7(11):e49528. doi: 10.1371/journal.pone.0049528. Epub 2012 Nov 19.
Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.
自 1996 年高致病性禽流感 H5N1 首次在中国南方出现以来，许多研究都在寻找风险因素，并基于环境和人为预测因素制作风险图。然而，人们对家禽养殖集约化程度与疫情爆发风险之间的联系关注甚少。本研究对 2004 年疫情第二波期间泰国中、西部的 H5N1 风险进行了重新测绘。使用一种基于每养殖场家禽数量的细分方法来量化生产结构。在分区和村庄两级，推导出了大规模和集约化养殖的鸭和鸡的种群密度。使用 Landsat 图像来推导出另一个以前被忽视的 HPAI H5N1 风险的潜在预测因子：洪水造成的景观中水的比例。我们使用了增强回归树模型的蒙特卡罗模拟方法来对预测变量进行特征化，以确定 HPAI H5N1 的风险。在分区和村庄两级，都得出了平均风险和不确定性的地图。增强回归树模型的整体准确性与逻辑回归方法相当。洪水造成的土地比例对预测疫情爆发的风险贡献最大，其次是密集养殖鸭、大规模养殖鸭和人口密度。我们的研究结果表明，只要村庄中 15%的土地被洪水淹没，就足以达到与该变量相关的最高风险水平。预测风险的空间模式与以前的工作相似：高风险地区主要位于湄南河洪泛平原以及曼谷东南部。使用高分辨率的村庄级家禽普查数据，而不是分区数据，提高了预测的空间准确性，突出了风险的局部变化。这些地图为指导干预提供了有用的信息。
