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10.1371/journal.pone.0249821 http://scihub22266oqcxt.onion/10.1371/journal.pone.0249821 33836017!8034715!33836017     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (4): e0249821 Nephropedia Template TP {{tp|p=33836017|t=2021. Modelling COVID-19 transmission in supermarkets using an agent-based model |pdf=|usr=}}{{33836017}} gab.com Text Modelling COVID-19 transmission in supermarkets using an agent-based model JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33836017 #FOAvip Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff. Twit Text FOAVip Modelling COVID-19 transmission in supermarkets using an agent-based model ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33836017 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33836017 #FOAvip English Wikipedia <ref>{{Cite pmid|33836017}}</ref> Modelling COVID-19 transmission in supermarkets using an agent-based model #MMPMID33836017 Ying F; O'Clery N PLoS One 2021[]; 16 (4): e0249821 PMID33836017show ga Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff. |*COVID-19/epidemiology/transmission[MESH] |*Consumer Behavior[MESH] |*Disease Outbreaks[MESH] |*Models, Biological[MESH] |*SARS-CoV-2[MESH] |*Supermarkets[MESH]Modelling COVID-19 transmission in supermarkets using an agent-based m(epmc).pdf DeepDyve Pubget Overpricing