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10.1016/j.matcom.2021.01.022 http://scihub22266oqcxt.onion/10.1016/j.matcom.2021.01.022 33612959!7881743!33612959     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 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       Math+Comput+Simul 2021 ; 185 (ä): 687-695 Nephropedia Template TP {{tp|p=33612959|t=2021. Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states |pdf=|usr=}}{{33612959}} gab.com Text Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states JO: Math Comput Simul http://www.kidney.de/pget.php?&tw=1&pmid=33612959 #FOAvip BACKGROUND: A Susceptible-Exposed-Infected-Removed? (SEIR) model was developed to forecast the spread of the novel coronavirus (SARS-CoV-2) in the United States and the implications of re-opening and hospital resource utilization. The model relies on the specification of various parameters that characterize the virus and the population being modeled. However, several of these parameters can be expected to vary significantly between states. Therefore, a genetic algorithm was developed that adjusts these population-dependent parameters to fit the SEIR model to data for any given state. METHODS: Publicly available data was collected from each state in terms of the number of positive COVID-19 cases and the number of COVID-19-caused deaths and used as inputs into a SEIR model to predict the spread of COVID infections in a given population. A genetic algorithm was designed where the genes are the state-dependent parameters from the model. The algorithm operates by determining the fitness of a given set of genes, applying selection, using selected agents to reproduce with cross-over, applying random mutation, and simulating several generations. FINDINGS AND CONCLUSIONS: Use of the genetic algorithm produces exceptionally good agreement between the model and available data. Deviations in the parameters were examined to see if the trends were reasonable. Twit Text FOAVip Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states ????Math Comput Simul http://www.kidney.de/pget.php?&tw=1&pmid=33612959 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33612959 #FOAvip English Wikipedia <ref>{{Cite pmid|33612959}}</ref> Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states #MMPMID33612959 Yarsky P Math Comput Simul 2021[Jul]; 185 (ä): 687-695 PMID33612959show ga BACKGROUND: A Susceptible-Exposed-Infected-Removed? (SEIR) model was developed to forecast the spread of the novel coronavirus (SARS-CoV-2) in the United States and the implications of re-opening and hospital resource utilization. The model relies on the specification of various parameters that characterize the virus and the population being modeled. However, several of these parameters can be expected to vary significantly between states. Therefore, a genetic algorithm was developed that adjusts these population-dependent parameters to fit the SEIR model to data for any given state. METHODS: Publicly available data was collected from each state in terms of the number of positive COVID-19 cases and the number of COVID-19-caused deaths and used as inputs into a SEIR model to predict the spread of COVID infections in a given population. A genetic algorithm was designed where the genes are the state-dependent parameters from the model. The algorithm operates by determining the fitness of a given set of genes, applying selection, using selected agents to reproduce with cross-over, applying random mutation, and simulating several generations. FINDINGS AND CONCLUSIONS: Use of the genetic algorithm produces exceptionally good agreement between the model and available data. Deviations in the parameters were examined to see if the trends were reasonable. äUsing a genetic algorithm to fit parameters of a COVID-19 SEIR model f(epmc).pdf DeepDyve Pubget Overpricing