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10.1007/s11071-021-06424-0 http://scihub22266oqcxt.onion/10.1007/s11071-021-06424-0 33867677!8038536!33867677     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33867677&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nonlinear+Dyn 2021 ; 106 (2): 1187-1195 Nephropedia Template TP {{tp|p=33867677|t=2021. Social distancing mediated generalized model to predict epidemic spread of COVID-19 |pdf=|usr=}}{{33867677}} gab.com Text Social distancing mediated generalized model to predict epidemic spread of COVID-19 JO: Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33867677 #FOAvip The extensive proliferation of recent coronavirus (COVID-19), all over the world, is the outcome of social interactions through massive transportation, gatherings and population growth. To disrupt the widespread of COVID-19, a mechanism for social distancing is indispensable. Also, to predict the effectiveness and quantity of social distancing for a particular social network, with a certain contagion, a generalized model is needed. In this manuscript, we propose a social distancing mediated generalized model to predict the pandemic spread of COVID-19. By considering growth rate as a temporal harmonic function damped with social distancing in generalized Richard model and by using the data of confirmed COVID-19 cases in China, USA and India, we find that, with time, the cumulative spread grows more rapidly due to weak social distancing as compared to the stronger social distancing, where it is explicitly decreasing. Furthermore, we predict the possible outcomes with various social distancing scenarios by considering highest growth rate as an initial state, and illustrate that the increase in social distancing tremendously decreases growth rate, even it tends to reach zero in lockdown regimes. Our findings not only provide epidemic growth scenarios as a function of social distancing but also provide a modified growth model to predict controlled information flow in any network. Twit Text FOAVip Social distancing mediated generalized model to predict epidemic spread of COVID-19 ????Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33867677 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33867677 #FOAvip English Wikipedia <ref>{{Cite pmid|33867677}}</ref> Social distancing mediated generalized model to predict epidemic spread of COVID-19 #MMPMID33867677 Yasir KA; Liu WM Nonlinear Dyn 2021[]; 106 (2): 1187-1195 PMID33867677show ga The extensive proliferation of recent coronavirus (COVID-19), all over the world, is the outcome of social interactions through massive transportation, gatherings and population growth. To disrupt the widespread of COVID-19, a mechanism for social distancing is indispensable. Also, to predict the effectiveness and quantity of social distancing for a particular social network, with a certain contagion, a generalized model is needed. In this manuscript, we propose a social distancing mediated generalized model to predict the pandemic spread of COVID-19. By considering growth rate as a temporal harmonic function damped with social distancing in generalized Richard model and by using the data of confirmed COVID-19 cases in China, USA and India, we find that, with time, the cumulative spread grows more rapidly due to weak social distancing as compared to the stronger social distancing, where it is explicitly decreasing. Furthermore, we predict the possible outcomes with various social distancing scenarios by considering highest growth rate as an initial state, and illustrate that the increase in social distancing tremendously decreases growth rate, even it tends to reach zero in lockdown regimes. Our findings not only provide epidemic growth scenarios as a function of social distancing but also provide a modified growth model to predict controlled information flow in any network. äSocial distancing mediated generalized model to predict epidemic sprea(epmc).pdf DeepDyve Pubget Overpricing