Department of Environmental and Occupational Health Sciences, Center for One Health Research, University of Washington, Seattle, 98195, USA.
Department of Epidemiology, University of Washington, Seattle, 98195, USA.
Sci Rep. 2022 Aug 3;12(1):13310. doi: 10.1038/s41598-022-16118-1.
More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo, and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.
超过 10 亿人依靠畜牧业获得收入、营养和社会凝聚力，但畜牧业可能促进疾病传播并导致气候变化。虽然关于牲畜分布的数据在广泛的应用中具有广泛的用途，但大规模绘制牲畜分布的努力仅限于全球网格化牲畜项目（GLW）。我们提出了一项补充工作，以绘制马拉维、乌干达、刚果民主共和国和南苏丹的牛和猪的分布情况。与 GLW 不同，GLW 使用非对称建模应用于普查数据来产生牲畜数量和空间密度的时间分层估计，我们的工作使用复杂的调查数据和不同的建模方法来生成牲畜分布的时间序列，将牲畜密度定义为动物与人类的比例。除了有利的交叉验证结果和与源自国家人口和牲畜的外部数据的国家密度估计的一般一致性外，我们的结果还与 GLW-3 估计值非常吻合，支持了这两项工作的有效性。我们的结果还提供了一个高分辨率的时间序列结果，并采用了一种密度定义，特别适合研究由牲畜引起的人畜共患病。
