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10.1016/j.jinf.2021.04.020 http://scihub22266oqcxt.onion/10.1016/j.jinf.2021.04.020 33895226!8061788!33895226     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33895226&cmd=llinks): Failed to open stream: HTTP request failed! 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Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - A high risk community-hospital interface |pdf=|usr=}}{{33895226}} gab.com Text Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - A high risk community-hospital interface JO: J Infect http://www.kidney.de/pget.php?&tw=1&pmid=33895226 #FOAvip OBJECTIVES: Patients requiring haemodialysis are at increased risk of serious illness with SARS-CoV-2 infection. To improve the understanding of transmission risks in six Scottish renal dialysis units, we utilised the rapid whole-genome sequencing data generated by the COG-UK consortium. METHODS: We combined geographical, temporal and genomic sequence data from the community and hospital to estimate the probability of infection originating from within the dialysis unit, the hospital or the community using Bayesian statistical modelling and compared these results to the details of epidemiological investigations. RESULTS: Of 671 patients, 60 (8.9%) became infected with SARS-CoV-2, of whom 16 (27%) died. Within-unit and community transmission were both evident and an instance of transmission from the wider hospital setting was also demonstrated. CONCLUSIONS: Near-real-time SARS-CoV-2 sequencing data can facilitate tailored infection prevention and control measures, which can be targeted at reducing risk in these settings. Twit Text FOAVip Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - A high risk community-hospital interface ????J Infect http://www.kidney.de/pget.php?&tw=1&pmid=33895226 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33895226 #FOAvip English Wikipedia <ref>{{Cite pmid|33895226}}</ref> Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - A high risk community-hospital interface #MMPMID33895226 Li KK; Woo YM; Stirrup O; Hughes J; Ho A; Filipe ADS; Johnson N; Smollett K; Mair D; Carmichael S; Tong L; Nichols J; Aranday-Cortes E; Brunker K; Parr YA; Nomikou K; McDonald SE; Niebel M; Asamaphan P; Sreenu VB; Robertson DL; Taggart A; Jesudason N; Shah R; Shepherd J; Singer J; Taylor AHM; Cousland Z; Price J; Lees JS; Jones TPW; Lopez CV; MacLean A; Starinskij I; Gunson R; Morris STW; Thomson PC; Geddes CC; Traynor JP; Breuer J; Thomson EC; Mark PB J Infect 2021[Jul]; 83 (1): 96-103 PMID33895226show ga OBJECTIVES: Patients requiring haemodialysis are at increased risk of serious illness with SARS-CoV-2 infection. To improve the understanding of transmission risks in six Scottish renal dialysis units, we utilised the rapid whole-genome sequencing data generated by the COG-UK consortium. METHODS: We combined geographical, temporal and genomic sequence data from the community and hospital to estimate the probability of infection originating from within the dialysis unit, the hospital or the community using Bayesian statistical modelling and compared these results to the details of epidemiological investigations. RESULTS: Of 671 patients, 60 (8.9%) became infected with SARS-CoV-2, of whom 16 (27%) died. Within-unit and community transmission were both evident and an instance of transmission from the wider hospital setting was also demonstrated. CONCLUSIONS: Near-real-time SARS-CoV-2 sequencing data can facilitate tailored infection prevention and control measures, which can be targeted at reducing risk in these settings. |*COVID-19[MESH] |*SARS-CoV-2[MESH] |Bayes Theorem[MESH] |Hospitals[MESH] |Humans[MESH] |Molecular Epidemiology[MESH]Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis unit(epmc).pdf DeepDyve Pubget Overpricing