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10.1007/s41403-020-00142-6 http://scihub22266oqcxt.onion/10.1007/s41403-020-00142-6 C7333587!7333587!38624421     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       ä-/-ä 2020 ; 5 (2): 117-27 Nephropedia Template TP {{tp|p=38624421|t=2020. Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization |pdf=|usr=}}{{38624421}} gab.com Text Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization JO: ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=38624421 #FOAvip Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs. Twit Text FOAVip Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization ????ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=38624421 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=38624421 #FOAvip English Wikipedia <ref>{{Cite pmid|38624421}}</ref> Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization #MMPMID38624421 Chandak A; Dey D; Mukhoty B; Kar P ä-/-ä 2020[]; 5 (2): 117-27 PMID38624421show ga Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs. äEpidemiologically and Socio-economically Optimal Policies via Bayesian(epmc).pdf DeepDyve Pubget Overpricing