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10.1007/s11071-020-05769-2 http://scihub22266oqcxt.onion/10.1007/s11071-020-05769-2 C7320847!7320847!32836809     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 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       Nonlinear+Dyn 2020 ; 101 (3): 1789-800 Nephropedia Template TP {{tp|p=32836809|t=2020. Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model |pdf=|usr=}}{{32836809}} gab.com Text Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model JO: Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32836809 #FOAvip Policy makers around the world are facing unprecedented challenges in making decisions on when and what degrees of measures should be implemented to tackle the COVID-19 pandemic. Here, using a nationwide mobile phone dataset, we developed a networked meta-population model to simulate the impact of intervention in controlling the spread of the virus in China by varying the effectiveness of transmission reduction and the timing of intervention start and relaxation. We estimated basic reproduction number and transition probabilities between health states based on reported cases. Our model demonstrates that both the time of initiating an intervention and its effectiveness had a very large impact on controlling the epidemic, and the current Chinese intense social distancing intervention has reduced the impact substantially but would have been even more effective had it started earlier. The optimal duration of the control measures to avoid resurgence was estimated to be 2 months, although would need to be longer under less effective controls.Electronic supplementary material: The online version of this article (10.1007/s11071-020-05769-2) contains supplementary material, which is available to authorized users. Twit Text FOAVip Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model ????Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32836809 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32836809 #FOAvip English Wikipedia <ref>{{Cite pmid|32836809}}</ref> Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model #MMPMID32836809 Zhang J; Dong L; Zhang Y; Chen X; Yao G; Han Z Nonlinear Dyn 2020[]; 101 (3): 1789-800 PMID32836809show ga Policy makers around the world are facing unprecedented challenges in making decisions on when and what degrees of measures should be implemented to tackle the COVID-19 pandemic. Here, using a nationwide mobile phone dataset, we developed a networked meta-population model to simulate the impact of intervention in controlling the spread of the virus in China by varying the effectiveness of transmission reduction and the timing of intervention start and relaxation. We estimated basic reproduction number and transition probabilities between health states based on reported cases. Our model demonstrates that both the time of initiating an intervention and its effectiveness had a very large impact on controlling the epidemic, and the current Chinese intense social distancing intervention has reduced the impact substantially but would have been even more effective had it started earlier. The optimal duration of the control measures to avoid resurgence was estimated to be 2 months, although would need to be longer under less effective controls.Electronic supplementary material: The online version of this article (10.1007/s11071-020-05769-2) contains supplementary material, which is available to authorized users. äInvestigating time, strength, and duration of measures in controlling (epmc).pdf DeepDyve Pubget Overpricing