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10.2807/1560-7917.ES.2020.25.16.2000155 http://scihub22266oqcxt.onion/10.2807/1560-7917.ES.2020.25.16.2000155 32347198!7189647!32347198     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Euro+Surveill 2020 ; 25 (16): ä Nephropedia Template TP {{tp|p=32347198|t=2020. Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020 |pdf=|usr=}}{{32347198}} gab.com Text Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020 JO: Euro Surveill http://www.kidney.de/pget.php?&tw=1&pmid=32347198 #FOAvip BackgroundCOVID-19, caused by SARS-CoV-2, first appeared in China and subsequently developed into an ongoing epidemic. Understanding epidemiological factors characterising the transmission dynamics of this disease is of fundamental importance.AimsThis study aimed to describe key epidemiological parameters of COVID-19 in Hong Kong.MethodsWe extracted data of confirmed COVID-19 cases and their close contacts from the publicly available information released by the Hong Kong Centre for Health Protection. We used doubly interval censored likelihood to estimate containment delay and serial interval, by fitting gamma, lognormal and Weibull distributions to respective empirical values using Bayesian framework with right truncation. A generalised linear regression model was employed to identify factors associated with containment delay. Secondary attack rate was also estimated.ResultsThe empirical containment delay was 6.39 days; whereas after adjusting for right truncation with the best-fit Weibull distribution, it was 10.4 days (95% CrI: 7.15 to 19.81). Containment delay increased significantly over time. Local source of infection and number of doctor consultations before isolation were associated with longer containment delay. The empirical serial interval was 4.58-6.06 days; whereas the best-fit lognormal distribution to 26 certain-and-probable infector-infectee paired data gave an estimate of 4.77 days (95% CrI: 3.47 to 6.90) with right-truncation. The secondary attack rate among close contacts was 11.7%.ConclusionWith a considerable containment delay and short serial interval, contact-tracing effectiveness may not be optimised to halt the transmission with rapid generations replacement. Our study highlights the transmission risk of social interaction and pivotal role of physical distancing in suppressing the epidemic. Twit Text FOAVip Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020 ????Euro Surveill http://www.kidney.de/pget.php?&tw=1&pmid=32347198 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32347198 #FOAvip English Wikipedia <ref>{{Cite pmid|32347198}}</ref> Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020 #MMPMID32347198 Kwok KO; Wong VWY; Wei WI; Wong SYS; Tang JW Euro Surveill 2020[Apr]; 25 (16): ä PMID32347198show ga BackgroundCOVID-19, caused by SARS-CoV-2, first appeared in China and subsequently developed into an ongoing epidemic. Understanding epidemiological factors characterising the transmission dynamics of this disease is of fundamental importance.AimsThis study aimed to describe key epidemiological parameters of COVID-19 in Hong Kong.MethodsWe extracted data of confirmed COVID-19 cases and their close contacts from the publicly available information released by the Hong Kong Centre for Health Protection. We used doubly interval censored likelihood to estimate containment delay and serial interval, by fitting gamma, lognormal and Weibull distributions to respective empirical values using Bayesian framework with right truncation. A generalised linear regression model was employed to identify factors associated with containment delay. Secondary attack rate was also estimated.ResultsThe empirical containment delay was 6.39 days; whereas after adjusting for right truncation with the best-fit Weibull distribution, it was 10.4 days (95% CrI: 7.15 to 19.81). Containment delay increased significantly over time. Local source of infection and number of doctor consultations before isolation were associated with longer containment delay. The empirical serial interval was 4.58-6.06 days; whereas the best-fit lognormal distribution to 26 certain-and-probable infector-infectee paired data gave an estimate of 4.77 days (95% CrI: 3.47 to 6.90) with right-truncation. The secondary attack rate among close contacts was 11.7%.ConclusionWith a considerable containment delay and short serial interval, contact-tracing effectiveness may not be optimised to halt the transmission with rapid generations replacement. Our study highlights the transmission risk of social interaction and pivotal role of physical distancing in suppressing the epidemic. |*Contact Tracing[MESH] |*Coronavirus Infections/diagnosis/epidemiology/transmission[MESH] |*Epidemiological Monitoring[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/diagnosis/epidemiology/transmission[MESH] |Adult[MESH] |Aged[MESH] |Betacoronavirus/genetics/isolation & purification[MESH] |COVID-19[MESH] |Female[MESH] |Hong Kong/epidemiology[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Patient Isolation[MESH] |Referral and Consultation[MESH] |SARS-CoV-2[MESH] |Time Factors[MESH]Epidemiological characteristics of the first 53 laboratory-confirmed c(epmc).pdf DeepDyve Pubget Overpricing