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10.1007/s42979-021-00619-3 http://scihub22266oqcxt.onion/10.1007/s42979-021-00619-3 33907736!8061453!33907736     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       SN+Comput+Sci 2021 ; 2 (3): 230 Nephropedia Template TP {{tp|p=33907736|t=2021. Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata |pdf=|usr=}}{{33907736}} gab.com Text Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33907736 #FOAvip Since March, 2020, Coronavirus disease (COVID-19) has been designated as a pandemic by World Health Organization. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, governments are adopting nationwide interventions, like lockdown, containment and quarantine, restrictions on travel, cancelling social events and extensive testing. To understand the effects of these measures on the control of the epidemic in a data-driven manner, we propose a probabilistic cellular automata (PCA) based epidemiological model. The transitions associated with the model is driven by data available on chronology, symptoms, pathogenesis and transmissivity of the virus. By arguing that the lattice-based model captures the features of the dynamics along with the existing fluctuations, we perform rigorous computational analyses of the model to take into account of the spatial dynamics of social distancing measures imposed on the people. Considering the probabilistic behavioral aspects associated with mitigation strategies, we study the model considering factors like population density and testing efficiency. Using the model, we focus on the variability of epidemic dynamics data for different countries, and point out the reasons behind these contrasting observations. To the best of our knowledge, this is the first attempt to model COVID-19 spread using PCA that gives us both spatial and temporal variations of the infection spread with the insight about the contributions of different infection parameters. Twit Text FOAVip Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33907736 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33907736 #FOAvip English Wikipedia <ref>{{Cite pmid|33907736}}</ref> Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata #MMPMID33907736 Ghosh S; Bhattacharya S SN Comput Sci 2021[]; 2 (3): 230 PMID33907736show ga Since March, 2020, Coronavirus disease (COVID-19) has been designated as a pandemic by World Health Organization. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, governments are adopting nationwide interventions, like lockdown, containment and quarantine, restrictions on travel, cancelling social events and extensive testing. To understand the effects of these measures on the control of the epidemic in a data-driven manner, we propose a probabilistic cellular automata (PCA) based epidemiological model. The transitions associated with the model is driven by data available on chronology, symptoms, pathogenesis and transmissivity of the virus. By arguing that the lattice-based model captures the features of the dynamics along with the existing fluctuations, we perform rigorous computational analyses of the model to take into account of the spatial dynamics of social distancing measures imposed on the people. Considering the probabilistic behavioral aspects associated with mitigation strategies, we study the model considering factors like population density and testing efficiency. Using the model, we focus on the variability of epidemic dynamics data for different countries, and point out the reasons behind these contrasting observations. To the best of our knowledge, this is the first attempt to model COVID-19 spread using PCA that gives us both spatial and temporal variations of the infection spread with the insight about the contributions of different infection parameters. äComputational Model on COVID-19 Pandemic Using Probabilistic Cellular (epmc).pdf DeepDyve Pubget Overpricing