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10.4081/jphr.2021.1906 http://scihub22266oqcxt.onion/10.4081/jphr.2021.1906 33849254!7967497!33849254     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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Public+Health+Res 2021 ; 10 (1): ä Nephropedia Template TP {{tp|p=33849254|t=2021. Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread |pdf=|usr=}}{{33849254}} gab.com Text Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread JO: J Public Health Res http://www.kidney.de/pget.php?&tw=1&pmid=33849254 #FOAvip BACKGROUND: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter. METHODS: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794-22,372] and 124,506 [95% CI 69,526-265,113], respectively. RESULTS: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34-3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65-0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. CONCLUSIONS: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread. Twit Text FOAVip Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread ????J Public Health Res http://www.kidney.de/pget.php?&tw=1&pmid=33849254 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33849254 #FOAvip English Wikipedia <ref>{{Cite pmid|33849254}}</ref> Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread #MMPMID33849254 Zhang H; Zhao X; Yin K; Yan Y; Qian W; Chen B; Fang X J Public Health Res 2021[Mar]; 10 (1): ä PMID33849254show ga BACKGROUND: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter. METHODS: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794-22,372] and 124,506 [95% CI 69,526-265,113], respectively. RESULTS: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34-3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65-0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. CONCLUSIONS: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread. äDynamic estimation of epidemiological parameters of COVID-19 outbreak (epmc).pdf DeepDyve Pubget Overpricing