The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach.

Mohammad Reza Davahli,Atsuo Murata,Tadeusz Marek,Nabin Sapkota,Krzysztof Fiok,Waldemar Karwowski,Redha Taiar,Farzad V Farahani,Awad Al-Juaid

doi:10.3390/biology11010125

Abstract

Simple SummaryIn this study, we conducted a quantitative assessment and compared the COVID-19 pandemic spread in two countries based on selected methods from the graph theory domain. The results indicate that while the applied experimental procedures are useful, we could draw limited conclusions about the dynamic nature of infection diffusion. We discussed the possible reasons for the above and used them to formulate research hypotheses that could serve the scientific community in future research efforts.Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

Highlights

We proposed a methodology called the pandemic diffusion network dynamics (PDND) approach to build diffusion graphs of the COVID-19 pandemic in the US and
We showed that graph neural networks—based on synchrony of COVID-19 time series data—can improve the accuracy of predicting COVID-19 dynamics [1]
Our study explores if the comparison of two networks and the respective phenomena of COVID-19 diffusion can be quantitatively assessed by means of the pandemic diffusion network dynamics (PDND) approach

Summary

Introduction

China officially reported the first case of a new coronavirus disease, COVID-19, on December 2019 [1]. As China failed to control the outbreak, the virus responsible for this creativecommons.org/licenses/by/ 4.0/). Disease, SARS-CoV-2, spread to many countries and was declared a global pandemic [2,3]. The US and Japan are among the countries affected by the SARS-CoV-2 virus. The US reported its first confirmed case of COVID-19 on 20 January 2020 [4]. By the end of January, the number of confirmed cases increased to six; the US government restricted travel from China and declared a public health emergency [5]. By the end of February, the number of confirmed COVID-19 cases had grown to 60; on

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