tetano
Editor, Senior Moderator
Biology (Basel)
. 2022 Jan 13;11(1):125.
doi: 10.3390/biology11010125.
The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach
Mohammad Reza Davahli[SUP] 1 [/SUP], Waldemar Karwowski[SUP] 1 [/SUP], Krzysztof Fiok[SUP] 1 [/SUP], Atsuo Murata[SUP] 2 [/SUP], Nabin Sapkota[SUP] 3 [/SUP], Farzad V Farahani[SUP] 4 [/SUP], Awad Al-Juaid[SUP] 5 [/SUP], Tadeusz Marek[SUP] 6 [/SUP], Redha Taiar[SUP] 7 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19 pandemic; graph theory; network density; pandemic diffusion.
. 2022 Jan 13;11(1):125.
doi: 10.3390/biology11010125.
The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach
Mohammad Reza Davahli[SUP] 1 [/SUP], Waldemar Karwowski[SUP] 1 [/SUP], Krzysztof Fiok[SUP] 1 [/SUP], Atsuo Murata[SUP] 2 [/SUP], Nabin Sapkota[SUP] 3 [/SUP], Farzad V Farahani[SUP] 4 [/SUP], Awad Al-Juaid[SUP] 5 [/SUP], Tadeusz Marek[SUP] 6 [/SUP], Redha Taiar[SUP] 7 [/SUP]
Affiliations
- PMID: 35053123
- DOI: 10.3390/biology11010125
Abstract
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.
Keywords: COVID-19 pandemic; graph theory; network density; pandemic diffusion.