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10.3390/jcm10061256 http://scihub22266oqcxt.onion/10.3390/jcm10061256 33803634!8003080!33803634     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 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       J+Clin+Med 2021 ; 10 (6): ä Nephropedia Template TP {{tp|p=33803634|t=2021. Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |pdf=|usr=}}{{33803634}} gab.com Text Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study JO: J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=33803634 #FOAvip Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts. Twit Text FOAVip Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study ????J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=33803634 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33803634 #FOAvip English Wikipedia <ref>{{Cite pmid|33803634}}</ref> Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study #MMPMID33803634 Nakajo K; Nishiura H J Clin Med 2021[Mar]; 10 (6): ä PMID33803634show ga Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts. äAssessing Interventions against Coronavirus Disease 2019 (COVID-19) in(epmc).pdf DeepDyve Pubget Overpricing