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10.1007/s11071-021-06248-y http://scihub22266oqcxt.onion/10.1007/s11071-021-06248-y 33551570!7849229!33551570     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       Nonlinear+Dyn 2021 ; 103 (3): 2955-2971 Nephropedia Template TP {{tp|p=33551570|t=2021. Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting |pdf=|usr=}}{{33551570}} gab.com Text Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting JO: Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33551570 #FOAvip The classic SIR model of epidemic dynamics is solved completely by quadratures, including a time integral transform expanded in a series of incomplete gamma functions. The model is also generalized to arbitrary time-dependent infection rates and solved explicitly when the control parameter depends on the accumulated infections at time t. Numerical results are presented by way of comparison. Autonomous and non-autonomous generalizations of SIR for interacting regions are also considered, including non-separability for two or more interacting regions. A reduction of simple SIR models to one variable leads us to a generalized logistic model, Richards model, which we use to fit Mexico's COVID-19 data up to day number 134. Forecasting scenarios resulting from various fittings are discussed. A critique to the applicability of these models to current pandemic outbreaks in terms of robustness is provided. Finally, we obtain the bifurcation diagram for a discretized version of Richards model, displaying period doubling bifurcation to chaos. Twit Text FOAVip Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting ????Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33551570 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33551570 #FOAvip English Wikipedia <ref>{{Cite pmid|33551570}}</ref> Exactly solvable SIR models, their extensions and their application to sensitive pandemic forecasting #MMPMID33551570 Sadurni E; Luna-Acosta G Nonlinear Dyn 2021[]; 103 (3): 2955-2971 PMID33551570show ga The classic SIR model of epidemic dynamics is solved completely by quadratures, including a time integral transform expanded in a series of incomplete gamma functions. The model is also generalized to arbitrary time-dependent infection rates and solved explicitly when the control parameter depends on the accumulated infections at time t. Numerical results are presented by way of comparison. Autonomous and non-autonomous generalizations of SIR for interacting regions are also considered, including non-separability for two or more interacting regions. A reduction of simple SIR models to one variable leads us to a generalized logistic model, Richards model, which we use to fit Mexico's COVID-19 data up to day number 134. Forecasting scenarios resulting from various fittings are discussed. A critique to the applicability of these models to current pandemic outbreaks in terms of robustness is provided. Finally, we obtain the bifurcation diagram for a discretized version of Richards model, displaying period doubling bifurcation to chaos. äExactly solvable SIR models, their extensions and their application to(epmc).pdf DeepDyve Pubget Overpricing