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10.1016/j.idm.2020.06.008 http://scihub22266oqcxt.onion/10.1016/j.idm.2020.06.008 C7334973!7334973!32691015     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Infect+Dis+Model 2020 ; 5 (ä): 409-41 Nephropedia Template TP {{tp|p=32691015|t=2020. Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example |pdf=|usr=}}{{32691015}} gab.com Text Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example JO: Infect Dis Model http://www.kidney.de/pget.php?&tw=1&pmid=32691015 #FOAvip During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic. Twit Text FOAVip Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example ????Infect Dis Model http://www.kidney.de/pget.php?&tw=1&pmid=32691015 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32691015 #FOAvip English Wikipedia <ref>{{Cite pmid|32691015}}</ref> Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example #MMPMID32691015 Overton CE; Stage HB; Ahmad S; Curran-Sebastian J; Dark P; Das R; Fearon E; Felton T; Fyles M; Gent N; Hall I; House T; Lewkowicz H; Pang X; Pellis L; Sawko R; Ustianowski A; Vekaria B; Webb L Infect Dis Model 2020[]; 5 (ä): 409-41 PMID32691015show ga During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic. äUsing statistics and mathematical modelling to understand infectious d(epmc).pdf DeepDyve Pubget Overpricing