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10.1016/j.chaos.2020.109939 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.109939 C7260573!7260573!32834574     free     free     free 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 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 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 138 (ä): 109939 Nephropedia Template TP {{tp|p=32834574|t=2020. Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic |pdf=|usr=}}{{32834574}} gab.com Text Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834574 #FOAvip ?The very first model to predict the minimum optimal time of the lockdown.?Deliberates salient features like silent carriers, sociability of newly infected person and unregistered deaths of coronavirus infectious people along with SIR model.?Based on the awareness and sustainability of disease, the number of suspected person varies with respect to time of spreading of virus. Hence, three phases are formed to define the suspected rate as it will not grow exponentially all the time.?At first Phase, when the awareness of newly discovered virus is at initial phases, the suspecting rate will grow exponentially. Suspected rate depends on sociability of infected person.?Second Phase is during lockdown when the social distancing is followed. In this phase, death cases are more as number of current infected person are at peak. Simultaneously, the number of recovered person per day will increase.?Third Phase is when there is reduction in number of active cases. Number of suspected cases will reduce depending upon the testing speed of medical team.?In order to calculate the optimal lockdown time Topt, the approach of proposed model is to find a situation when the number suspected people starts to reduce w.r.t. time, number of infected people increases for a while but start reducing with respect to time, and also the number of recovered people rises w.r.t. time.?The model is tested on China data and is efficient enough to propose a model very close to their actual figures of infected people, recovered people, died and active cases. The model predicts the optimal lockdown period as 73 days for China which is very close to their actual lockdown period (77 days).?Also, the model is implemented to predict the optimal lockdown period of India and Italy.?The programming part is done in C++ language with Linux OS. Twit Text FOAVip Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834574 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834574 #FOAvip English Wikipedia <ref>{{Cite pmid|32834574}}</ref> Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic #MMPMID32834574 Lalwani S; Sahni G; Mewara B; Kumar R Chaos Solitons Fractals 2020[Sep]; 138 (ä): 109939 PMID32834574show ga ?The very first model to predict the minimum optimal time of the lockdown.?Deliberates salient features like silent carriers, sociability of newly infected person and unregistered deaths of coronavirus infectious people along with SIR model.?Based on the awareness and sustainability of disease, the number of suspected person varies with respect to time of spreading of virus. Hence, three phases are formed to define the suspected rate as it will not grow exponentially all the time.?At first Phase, when the awareness of newly discovered virus is at initial phases, the suspecting rate will grow exponentially. Suspected rate depends on sociability of infected person.?Second Phase is during lockdown when the social distancing is followed. In this phase, death cases are more as number of current infected person are at peak. Simultaneously, the number of recovered person per day will increase.?Third Phase is when there is reduction in number of active cases. Number of suspected cases will reduce depending upon the testing speed of medical team.?In order to calculate the optimal lockdown time Topt, the approach of proposed model is to find a situation when the number suspected people starts to reduce w.r.t. time, number of infected people increases for a while but start reducing with respect to time, and also the number of recovered people rises w.r.t. time.?The model is tested on China data and is efficient enough to propose a model very close to their actual figures of infected people, recovered people, died and active cases. The model predicts the optimal lockdown period as 73 days for China which is very close to their actual lockdown period (77 days).?Also, the model is implemented to predict the optimal lockdown period of India and Italy.?The programming part is done in C++ language with Linux OS. äPredicting optimal lockdown period with parametric approach using thre(epmc).pdf DeepDyve Pubget Overpricing