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10.3390/biology11010125 http://scihub22266oqcxt.onion/10.3390/biology11010125 35053123!8773348!35053123     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biology+(Basel) 2022 ; 11 (1): ä Nephropedia Template TP {{tp|p=35053123|t=2022. The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach |pdf=|usr=}}{{35053123}} gab.com Text The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach JO: Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=35053123 #FOAvip 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. Twit Text FOAVip The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach ????Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=35053123 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35053123 #FOAvip English Wikipedia <ref>{{Cite pmid|35053123}}</ref> The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach #MMPMID35053123 Davahli MR; Karwowski W; Fiok K; Murata A; Sapkota N; Farahani FV; Al-Juaid A; Marek T; Taiar R Biology (Basel) 2022[Jan]; 11 (1): ä PMID35053123show ga 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. äThe COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoreti(epmc).pdf DeepDyve Pubget Overpricing