TYPE Brief Research Report PUBLISHED 03 August 2022 DOI 10.3389/fpubh.2022.911336 OPEN ACCESS EDITED BY Wai Kit Ming, Jinan University, China REVIEWED BY Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil Gour Gobinda Goswami, North South University, Bangladesh *CORRESPONDENCE Sumiko Anno sumiko_anno@sophia.ac.jp SPECIALTY SECTION This article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health RECEIVED 02 April 2022 ACCEPTED 16 June 2022 PUBLISHED 03 August 2022 CITATION Anno S, Hirakawa T, Sugita S and Yasumoto S (2022) A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries. Front. Public Health 10:911336. doi: 10.3389/fpubh.2022.911336 COPYRIGHT © 2022 Anno, Hirakawa, Sugita and Yasumoto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries Sumiko Anno 1 *, Tsubasa Hirakawa 2 , Satoru Sugita 2 and Shinya Yasumoto 2 1 Graduate School of Global Environmental Studies, Sophia University, Tokyo, Japan, 2 Chubu Institute for Advanced Studies, Chubu University, Kasugai, Japan Introduction: Coronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue. Methods: This study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error. Results: Our results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability. Discussion: The proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies. KEYWORDS COVID-19, deep learning, graph convolutional network, predicting, public transportation Frontiers in Public Health 01 frontiersin.org