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10.1038/s41467-021-21809-w http://scihub22266oqcxt.onion/10.1038/s41467-021-21809-w 33712583!7955065!33712583     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2021 ; 12 (1): 1655 Nephropedia Template TP {{tp|p=33712583|t=2021. Digital proximity tracing on empirical contact networks for pandemic control |pdf=|usr=}}{{33712583}} gab.com Text Digital proximity tracing on empirical contact networks for pandemic control JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33712583 #FOAvip Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide. Twit Text FOAVip Digital proximity tracing on empirical contact networks for pandemic control ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33712583 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33712583 #FOAvip English Wikipedia <ref>{{Cite pmid|33712583}}</ref> Digital proximity tracing on empirical contact networks for pandemic control #MMPMID33712583 Cencetti G; Santin G; Longa A; Pigani E; Barrat A; Cattuto C; Lehmann S; Salathe M; Lepri B Nat Commun 2021[Mar]; 12 (1): 1655 PMID33712583show ga Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide. |*Pandemics/prevention & control/statistics & numerical data[MESH] |*SARS-CoV-2[MESH] |Basic Reproduction Number/prevention & control/statistics & numerical data[MESH] |COVID-19/epidemiology/*prevention & control/transmission[MESH] |Computer Simulation[MESH] |Contact Tracing/*methods/statistics & numerical data[MESH] |Humans[MESH] |Models, Statistical[MESH] |Privacy[MESH] |Quarantine/methods/statistics & numerical data[MESH]Digital proximity tracing on empirical contact networks for pandemic c(epmc).pdf DeepDyve Pubget Overpricing