Predicting confirmed cases of various epidemics using global temporal-feature-based graph convolutional network

Jisu Kang, Jin Sob Kim, Hyun Joon Park, Soyeon Lee, Ye Ji Han, Yajun Mei, Sung Won Han

Abstract: Highlights•We have proposed GLOGCN, which utilizes both global and local temporal features to predict various epidemics.•Experiments across epidemic datasets demonstrate the performance of GLOGCN.•The robustness and real-world applicability of the model are verified.