Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties
Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties
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DOI：
10.1109/bigdata55660.2022.10020969
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Shailik Sarkar;Abdulaziz Alhamadani;Chang-Tien Lu
中科院分区：
文献类型：
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作者：
Shailik Sarkar;Abdulaziz Alhamadani;Chang-Tien Lu
自新冠肺炎爆发以来，各种工作都集中在利用各种不同的静态和动态特征来辅助疾病预测模型的预测。然而，在缺乏历史大流行数据的情况下，这些模型将无法根据先前存在的情况，对最有可能受到影响的地区提供任何有意义的见解。此外，神经网络的黑匣子性质经常成为有关当局从中获得任何含义的障碍。在本文中，我们提出了一个新的可解释图神经网络框架称为图-新冠肺炎-解释器(GC-解释器)，它给出了对初始爆发期间传播严重程度的可解释预测。我们利用一组全面的静态种群特征作为GRAPH的节点特征，其中每个节点对应一个地理区域。与GNN解释的后处理方法不同，我们提出了一个框架，用于在模型的训练过程中学习重要特征。我们进一步将我们的模型应用于真实世界的早期大流行数据，以显示我们方法的有效性。通过GC-Explainer，我们表明，静态特征以及区域之间的空间相关性可以用来解释大流行早期爆发的不同严重程度，并提供一个框架来识别任何传染病爆发的高危地区，特别是在没有历史数据的情况下。
Ever since the COVID-19 outbreak, various works have focused on using multitude of different static and dynamic features to aid the prediction of disease forecasting models. However, in the absence of historical pandemic data these models will not be able to give any meaningful insight about the areas which are most likely to be affected based on preexisting conditions. Furthermore, the black box nature of neural networks often becomes an impediment for the concerned authorities to derive any meaning from. In this paper, we propose a novel explainable Graph Neural Network (GNN) framework called Graph-COVID-19-Explainer (GC-Explainer) that gives explainable prediction for the severity of the spread during initial outbreak. We utilize a comprehensive set of static population characteristics to use as node features of Graph where each node corresponds to a geographical region. Unlike post-hoc methods of GNN explanations, we propose a framework for learning important features during the training of the model. We further apply our model on real-world early pandemic data to show the validity of our approach. Through GC-Explainer, we show that static features along with spatial dependency among regions can be used to explain the varied degree of severity in outbreak during the early part of the pandemic and provide a framework to identify the at-risk areas for any infectious disease outbreak, especially when historical data is not available.