Deep Landscape Features for Improving Vector-borne Disease Prediction
Deep Landscape Features for Improving Vector-borne Disease Prediction
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发表时间：
2019-04
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通讯作者：
N. Rehman;U. Saif;R. Chunara
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作者：
N. Rehman;U. Saif;R. Chunara
全球面临登革热、黄热病、基孔肯雅热和寨卡等蚊媒疾病风险的人口正在扩大。传染病模型通常包括温度和降水等环境指标。鉴于高分辨率卫星图像的可用性不断增加，我们考虑将卫星图像中的景观特征纳入传染病预测模型中。要做到这一点，我们实现了一个卷积神经网络（CNN）模型Imagenet数据和标记的景观特征的卫星数据从伦敦。然后，我们将来自巴基斯坦的卫星图像数据的景观特征，使用CNN标记，在一个众所周知的易感-传染-传染病流行模型中，以及巴基斯坦2012-2016年的登革热病例数据。我们研究改进的预测模型为每个单独的景观功能，并评估使用图像标签从不同的地方的可行性。我们发现，将卫星衍生的景观特征，可以提高预测的爆发，这是重要的前瞻性和战略性的监测和控制方案。
The global population at risk of mosquito-borne diseases such as dengue, yellow fever, chikungunya and Zika is expanding. Infectious disease models commonly incorporate environmental measures like temperature and precipitation. Given increasing availability of high-resolution satellite imagery, here we consider including landscape features from satellite imagery into infectious disease prediction models. To do so, we implement a Convolutional Neural Network (CNN) model trained on Imagenet data and labelled landscape features in satellite data from London. We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan. We study improvement of the prediction model for each of the individual landscape features, and assess the feasibility of using image labels from a different place. We find that incorporating satellite-derived landscape features can improve prediction of outbreaks, which is important for proactive and strategic surveillance and control programmes.