COVID-19 infection inference with graph neural networks

Abstract

Infectious diseases spread rapidly, and epidemiological surveys are vital to detect high-risk transmitters and reduce transmission rates. To enhance efficiency and reduce the burden on epidemiologists, an automatic tool to assist with epidemiological surveys is necessary. This study aims to develop an automatic epidemiological survey to predict the influence of COVID-19-infected patients on future additional infections. To achieve this, the study utilized a dataset containing interaction information between confirmed cases, including contact order, contact times, and movement routes, as well as individual properties such as symptoms. Graph neural networks (GNNs) were used to incorporate interaction information and individual properties. Two variants of GNNs, graph convolutional and graph attention networks, were utilized, and the results showed that the graph-based models outperformed traditional machine learning models. For the area under the curve, the 2nd, 3rd, and 4th order spreading predictions showed higher performance by 0.200, 0.269, and 0.190, respectively. The results show that the contact information of an infected person is crucial data that can help predict whether that person will affect future infections. Our findings suggest that incorporating the relationships between an infected person and others can improve the effectiveness of an automatic epidemiological survey.