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10.3760/cma.j.cn112338-20200313-00331 http://scihub22266oqcxt.onion/10.3760/cma.j.cn112338-20200313-00331 32741161!ä!32741161 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Zhonghua+Liu+Xing+Bing+Xue+Za+Zhi 2020 ; 41 (7): 1000-1003 Nephropedia Template TP {{tp|p=32741161|t=2020. Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method |pdf=|usr=}}{{32741161}} gab.com Text Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method JO: Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32741161 #FOAvip Objectives: The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19. Methods: The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method. Results: A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 (P(25)-P(75): 3.472-7.318) days, 5.083 (P(25)-P(75): 3.511-7.314) days, and 5.695 (P(25)-P(75): 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions: The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution. Twit Text FOAVip Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method ????Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32741161 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32741161 #FOAvip English Wikipedia <ref>{{Cite pmid|32741161}}</ref> Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method #MMPMID32741161 Du ZC; Gu J; Li JH; Lin X; Wang Y; Chen L; Hao YT Zhonghua Liu Xing Bing Xue Za Zhi 2020[Jul]; 41 (7): 1000-1003 PMID32741161show ga Objectives: The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19. Methods: The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method. Results: A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 (P(25)-P(75): 3.472-7.318) days, 5.083 (P(25)-P(75): 3.511-7.314) days, and 5.695 (P(25)-P(75): 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions: The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution. |*Infectious Disease Incubation Period[MESH] |Adult[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Humans[MESH] |Likelihood Functions[MESH] |Pandemics[MESH] Estimating the distribution of COVID-19 incubation period by interval(epmc).pdf DeepDyve Pubget Overpricing