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10.3760/cma.j.cn112338-20200409-00540 http://scihub22266oqcxt.onion/10.3760/cma.j.cn112338-20200409-00540 32867427!ä!32867427 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 (8): 1220-1224 Nephropedia Template TP {{tp|p=32867427|t=2020. Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform |pdf=|usr=}}{{32867427}} gab.com Text Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform JO: Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32867427 #FOAvip Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved. Twit Text FOAVip Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform ????Zhonghua Liu Xing Bing Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32867427 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32867427 #FOAvip English Wikipedia <ref>{{Cite pmid|32867427}}</ref> Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform #MMPMID32867427 Sun YX; Shen P; Zhang JY; Lu P; Chai PF; Mou H; Huang WZ; Lin HB; Shui LM Zhonghua Liu Xing Bing Xue Za Zhi 2020[Aug]; 41 (8): 1220-1224 PMID32867427show ga Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved. |*Betacoronavirus/genetics/isolation & purification[MESH] |Big Data[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Cities[MESH] |Coronavirus Infections/*epidemiology[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Population Surveillance[MESH] |RNA, Viral/genetics/isolation & purification[MESH] |Real-Time Polymerase Chain Reaction[MESH] Epidemiological characteristics of COVID-19 monitoring cases in Yinzh(epmc).pdf DeepDyve Pubget Overpricing