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.
目的： 分析鄞州区基于健康大数据平台的新型冠状病毒肺炎（COVID-19）监测病例流行特征，为COVID-19监测网络体系建设提供依据。 方法： 收集鄞州区新型冠状病毒肺炎监测与预警信息系统每日COVID-19监测病例数据，分析COVID-19监测病例人群构成、流行病学史比例、核酸检测率、核酸阳性检出率和确诊病例监测发现率。 结果： 2020年1月1日至3月30日共报告COVID-19监测病例1 595例，其中社区人群和重点人群分别占79.94%和20.06%。监测病例现场调查核实率100.00%，有武汉市或湖北省接触流行病学史占6.27%，社区和重点人群中有流行病学史者占比分别为2.12%和22.81%（P<0.001）。COVID-19核酸总检测率18.24%（291/1 595），有、无流行病学史者核酸检测率分别为53.00%和15.92%（P<0.001），COVID-19核酸阳性检出率1.72%（5/291）。监测确诊病例发现率0.31%（5/1 595），监测确诊病例和其他确诊病例初次就诊至初次核酸检测时间间隔差异无统计学意义（P>0.05）。 结论： 基于健康大数据平台的COVID-19监测工作运转良好，但确诊病例监测发现率有待提高。.
Keywords: COVID-19; Epidemiological characteristics; Health big data; Monitoring.