基于发热门诊留观数据的大连市新型冠状病毒感染本地暴发疫情预警3种方法比较
Comparison of 3 early warning methods for COVID-19 outbreak in Dalian City based on observation data in the fever clinic
安庆玉 1吴隽 1郭俐男1
作者信息
- 1. 116023,辽宁省大连市疾病预防控制中心应急工作部
- 折叠
摘要
目的 以发热门诊留观病例为基础数据,筛选出应用于大连市新型冠状病毒感染本地暴发疫情预警的最佳方法.方法 分别采用Poisson分布、累计和控制图、指数加权移动平均法3种方法建立新型冠状病毒感染本地暴发疫情预警模型,并以灵敏度、特异度、约登指数、阳性预测值、阴性预测值等指标为评价指标,从中选择最优预警模型.结果 以2021年7月1日—12月19日和2022年2月24日—7月21日期间大连市28家医疗机构上报的发热门诊留观病例为基础数据,建立了3种预警模型.结果 显示,Poisson分布预警模型中,当检验水准取值为0.03时,预警灵敏度可达94.59％,特异度为41.38％,阳性预测值为33.98％,阴性预测值为96.00％,在2021年7月1日—12月19日和2022年2月24日—7月21日期间的3起本地暴发疫情中,均最早提前6 d提出预警信号;累计和控制图预警模型中,当允偏量设置为1.5和判定值设置为2时,模型的约登指数最大(0.17),模型的预警灵敏度为21.67％,特异度为95.12％,阳性预测值为52.00％,阴性预测值为83.27％,并在2021年的11月份疫情中分别提前2 d和1 d提出预警信号;指数加权移动平均法预警模型中,当权重因子λ设置为0.6和0.7,标准差系数设置为1时约登指数最大(0.16),模型的预警灵敏度为35.48％,特异度为80.33％,阳性预测值为31.43％,阴性预测值为83.05％,并在2021年11月和2022年4月疫情中分别最早6 d和5 d提出预警信号.结论 以发热门诊留观病例作为基础数据,采用Poisson分布建立新型冠状病毒感染暴发疫情预警模型,相对于累计和控制图法和指数加权移动平均法可获得较理想的预警效果.
Abstract
Objective Based on the data of observation cases in the fever clinic, to identify the best methods for early warning of COVID-19 outbreak in Dalian city, Liaoning province. Methods Poisson distribution, cumulative sum and exponentially weighted moving average were used to establish an early warning model for COVID-19 outbreak in Dalian City and to evaluate and compare the effects of early warning in terms of the sensitivity, specificity, Yoden index and positive predictive value and so on. Results Based on the data from July 1, 2021 to December 19, 2021 and February 24, 2022 to July 21, 2022, 28 medical institutions in Dalian reported observation cases in the fever clinic, three early warning models were established. The result showed that Poisson distribution model when the a=0.03, sensitivity, specificity, positive predictive value and negative predictive value was 94.59％, 41.38％, 33.98％ and 96.00％ respectively, and early warning signals issued 6 days before the 3 actual COVID-19 outbreak. CUSUM model when K=1.5, H=2, sensitivity, specificity, positive predictive value and negative predictive value of C2 was 21.67％, 95.12％, 52％ and 83.27％, respectively, the early warning signals issued 2 days and 1 day before the actual COVID-19 outbreak in November 2021. EWMA model when λ=0.6 or 0.7, K=1, sensitivity, specificity, positive predictive value and negative predictive value was 35.48％, 80.33％, 31.43％ and 83.05％ respectively, the early warning signals issued 6 days and 5 days before the actual COVID-19 outbreak in November 2021 and April 2022. Conclusions Compared with the CUSUM and EWMA model , the Poisson distribution model based on the observation cases in the fever clinic was effective and reliable in early-warning the COVID-19 outbreak in Dalian city. In the practical application process, if the surveillance data of fever clinic is informationized and real-time analysis of the surveillance data is realized, the early warning response can be further improved.
关键词
发热门诊/留观病例/新型冠状病毒感染/暴发疫情/预警Key words
fever clinic/observation cases/COVID-19/outbreak/early warning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据