A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries

Abstract

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.MethodsThis 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.ResultsOur 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.DiscussionThe 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.

Analysis And Research on The Influence of The Social Network on Public Opinion and The Transmission Path of The Epidemic

“…By analyzing changes in the daily number of newly diagnosed COVID-19 cases in 190 countries or regions over a 20-month period and the spatial relationship between airways, railways, and roads in regions or countries. The number of future COVID-19 cases on the eighth day is based on data from the previous 7 days corresponding to 190 regions or countries and the results proved to be broadly consistent with the real data [4]. The network has demonstrated its importance in predicting new crown infections, which advantageously promotes solutions for public health response to COVID-19 and helps public health policymakers make the right decisions in epidemic prevention and control.…”

Identification of targets for drug repurposing to treat COVID-19 using a Deep Learning Neural Network

“…Each neural network layer can be written as a non-linear function (4) [29], as shown below, where H (0) = X and H(l) = Z (or z for graph-level outputs), and l is the number of layers.…”

A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities

“…The topology of such networks has a significant impact on the epidemic's spread ( Moreno, Pastor-Satorras, & Vespignani, 2002 ). Data-driven network-based models were frequently combined with machine learning and deep learning techniques to uncover important patterns and conclusions in specific geographical districts ( Anno, Hirakawa, Sugita, & Yasumoto, 2022 ; Ojugo & Nwankwo, 2021 ; Pinheiro, Galati, Summerville, & Lambrecht, 2021 ; Roy, Biswas, & Ghosh, 2021 ). Another common modeling method that has been used in epidemiological studies is agent-based modeling ( Chen et al., 2022 ; Patel et al., 2021 ; Vedam & Ghose, 2022 ).…”