Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium.
Data Science Institute, Hasselt University, Hasselt, Belgium.
PLoS Comput Biol. 2024 Oct 1;20(10):e1011980. doi: 10.1371/journal.pcbi.1011980. eCollection 2024 Oct.
The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.
农场位置和规模的分布对于描述疾病传播模式至关重要。随着一些地区的畜牧业生产迅速集约化，导致农场在城市周边地区聚集程度增加，衡量农场空间分布的变化对于设计有效的干预措施至关重要。然而，在许多国家，这些数据并不存在，因为生成这些数据需要大量资源。在这里，我们开发了一种农场分布模型（FDM），可以在数据稀缺的国家预测家禽农场的位置和规模。该模型结合了（i）对数高斯 Cox 过程模型，用于模拟农场分布作为空间泊松点过程，以及（ii）随机森林模型，用于模拟农场规模（即每个农场的动物数量）。空间预测因子用于在孟加拉国、古吉拉特邦（印度邦）和泰国的密集肉鸡和蛋鸡农场分布上校准 FDM。FDM 在农场的空间聚类、位置和规模方面产生了现实的农场分布，同时提供了有关影响这些分布的因素的见解。最后，我们通过在一系列农场空间分布上模拟病原体传播，说明了在流行病传播背景下建模现实农场分布的相关性。我们发现，FDM 生成的农场分布与使用观测数据进行的模拟产生的传播模式一致，而随机点模式低估了大规模爆发的可能性。事实上，农场分布的空间聚类增加了传染病的易感性，突出了在流行病学模型研究中需要考虑这一点。由于 FDM 保持了农场位置和规模的现实分布，因此特别适用于缺乏这些数据的地区，可用于传染病传播的数学模型。
