Predicting COVID-19 using lioness optimization algorithm and graph convolution network

Abstract

In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.

Advances in the Development of Representation Learning and Its Innovations against COVID-19

“…To effectively capture potential information about virus transmission and capture the linearity and nonlinearity present in time series, Ntemi et al [160] proposed a hybrid model composed of a GCN, an LSTM, and an autoregressive filter to more accurately predict the number of cases. Li et al [161] proposed a prediction model that combines the lioness optimization algorithm with the graph convolutional network, which can capture spatiotemporal information from feature data to achieve accurate predictions of COVID-19 case numbers. Skianis et al [162] developed a multi-scale graph model utilizing demographic data, medical facilities, socio-economic indicators, and other related information to improve the prediction accuracy of COVID-19 positive cases and hospitalizations.…”