Dataset Information

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.

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.

SUBMITTER: Anno S

PROVIDER: S-EPMC9381970 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Anno Sumiko S Hirakawa Tsubasa T Sugita Satoru S Yasumoto Shinya S

Frontiers in public health 20220803

<h4>Introduction</h4>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 <i>via</i> 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 i ...[more]

PMID: 35991015

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Project description:MotivationHuman microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task.ResultsIn this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network and a microbe-drug interaction network. We then proposed a novel graph convolutional network (GCN)-based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e. microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart-based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes, respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e. Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations.Availability and implementationPython codes and dataset are available at: https://github.com/longyahui/GCNMDA.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Introduction

Methods

Results

Discussion

Publications

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

Similar Datasets

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