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10.1371/journal.pcbi.1009351 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1009351 34460813!8432902!34460813     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       PLoS+Comput+Biol 2021 ; 17 (8): e1009351 Nephropedia Template TP {{tp|p=34460813|t=2021. Targeted pandemic containment through identifying local contact network bottlenecks |pdf=|usr=}}{{34460813}} gab.com Text Targeted pandemic containment through identifying local contact network bottlenecks JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34460813 #FOAvip Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts-between individuals or between population centres-are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods. Twit Text FOAVip Targeted pandemic containment through identifying local contact network bottlenecks ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34460813 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34460813 #FOAvip English Wikipedia <ref>{{Cite pmid|34460813}}</ref> Targeted pandemic containment through identifying local contact network bottlenecks #MMPMID34460813 Yang S; Senapati P; Wang D; Bauch CT; Fountoulakis K PLoS Comput Biol 2021[Aug]; 17 (8): e1009351 PMID34460813show ga Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts-between individuals or between population centres-are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods. |*Computer Simulation[MESH] |*Models, Biological[MESH] |Algorithms[MESH] |COVID-19/epidemiology/*prevention & control/transmission[MESH] |Humans[MESH] |Oregon/epidemiology[MESH] |Pandemics[MESH] |Quebec/epidemiology[MESH]Targeted pandemic containment through identifying local contact networ(epmc).pdf DeepDyve Pubget Overpricing