Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision.
Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision.
复制标题
使用计算机视觉预测人畜共患病水库的精细空间分布。
DOI：
10.1111/ele.14307
复制
发表时间：
2023
期刊：
影响因子：
8.8
通讯作者：
Nuismer,ScottL
中科院分区：
文献类型：
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作者：
Layman,NathanC;Basinski,AndrewJ;Zhang,Boyu;Eskew,EvanA;Bird,BrianH;Ghersi,BrunoM;Bangura,James;Fichet-Calvet,Elisabeth;Remien,ChristopherH;Vandi,Mohamed;Bah,Mohamed;Nuismer,ScottL
人畜共患疾病威胁着全世界的人类健康，而且往往与人为干扰有关。预测干扰如何影响溢出风险对于有效的疾病干预至关重要，但在精细的空间尺度上难以实现。在这里，我们开发了一种方法，从航空图像中学习水库物种的空间分布。我们的方法使用神经网络从图像中提取已知或假设重要性的特征。这些功能的空间分布，然后总结和链接到空间上明确的储层存在/不存在的数据，使用提升回归树。我们证明了我们的方法的实用性，将其应用到水库的拉沙病毒，Mastomys natalensis，在西非国家塞拉利昂和几内亚。我们表明，当使用水库捕获数据和公开的航空图像进行训练时，我们的框架学习环境特征与水库发生之间的关系，并根据水库存在的可能性对区域进行准确排名。
Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus,Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.