Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.3390/ijerph17124182 http://scihub22266oqcxt.onion/10.3390/ijerph17124182 32545441!7344815!32545441     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (12): ä Nephropedia Template TP {{tp|p=32545441|t=2020. Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution |pdf=|usr=}}{{32545441}} gab.com Text Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32545441 #FOAvip The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data. Twit Text FOAVip Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32545441 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32545441 #FOAvip English Wikipedia <ref>{{Cite pmid|32545441}}</ref> Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution #MMPMID32545441 Milano M; Cannataro M Int J Environ Res Public Health 2020[Jun]; 17 (12): ä PMID32545441show ga The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Data Interpretation, Statistical[MESH] |Hospitalization/*trends[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH]Statistical and Network-Based Analysis of Italian COVID-19 Data: Commu(epmc).pdf DeepDyve Pubget Overpricing