论文标题
具有空间和气候耦合的综合复发网络和回归模型，用于媒介传播疾病动力学
An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
论文作者
论文摘要
我们开发了一个综合复发性神经网络和非线性回归时空模型，用于媒介传播疾病的演变。我们将气候数据和季节性视为与传播昆虫（例如苍蝇）相关的外部因素，也是来自感兴趣地区周围邻近地区的溢出感染。气候数据是通过推荐系统激励的二次嵌入方案编码到模型的。相邻区域的影响是由长期短期记忆神经网络建模的。综合模型是通过随机梯度下降训练的，并从2013 - 2018年在斯里兰卡的Leish-Maniasis数据进行了测试，发生了感染暴发。我们的模型在许多具有高感染的地区的ARIMA模型优于Arima模型，相关的消融研究为我们的建模假设和思想提供了支持。
We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution. We take into account climate data and seasonality as external factors that correlate with disease transmitting insects (e.g. flies), also spill-over infections from neighboring regions surrounding a region of interest. The climate data is encoded to the model through a quadratic embedding scheme motivated by recommendation systems. The neighboring regions' influence is modeled by a long short-term memory neural network. The integrated model is trained by stochastic gradient descent and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred. Our model outperformed ARIMA models across a number of regions with high infections, and an associated ablation study renders support to our modeling hypothesis and ideas.
