TY - JOUR AU - Silenou, Bernard C AU - Verset, Carolin AU - Kaburi, Basil B AU - Leuci, Olivier AU - Ghozzi, Stéphane AU - Duboudin, Cédric AU - Krause, Gérard PY - 2022 DA - 2022/5/31 TI -使用接触追踪数据实时估计传染病流行病学参数的新工具:开发和部署乔- JMIR公共卫生Surveill SP - e34438六世- 8 - 5 KW - COVID-19 KW -疾病暴发KW -接触者追踪KW -连续间隔KW -基本再生数千瓦-传染病潜伏期千瓦superspreading事件KW -远程医疗KW -公共卫生KW -流行病学KW -监视工具KW -疫情应对KW -流行KW -数字医疗应用KW -响应策略AB -背景:疫情监测应对管理和分析系统(SORMAS)包含一个管理模块，用于支持各国应对疫情。它包括病例、联系人和事件的记录、联系和跟踪。为了使SORMAS用户能够可视化数据，计算基本监测指标，并从此类网络数据实时估计流行病学参数，我们开发了SORMAS Statistics (SORMAS- stats)应用程序。目的:本研究旨在描述SORMAS-Stats应用程序中实现的基本可视化、监测指标和流行病学参数，并说明SORMAS-Stats在应对COVID-19疫情中的应用。方法:基于快速回顾和SORMAS用户请求的结果，我们在SORMAS- stats中包括以下参数的可视化和估计:传输网络图、串行间隔(SI)、时变复制数R(t)、分散参数k以及以图表和表格形式显示的其他监测指标。我们通过将对数正态分布、伽玛分布和威布尔分布拟合到观察到的感染者-感染者对症状发作日期之间的天数分布来估计SI。我们通过拟合一个负二项分布到每个感染者的观察数量来估计k。此外，我们应用马尔可夫链蒙特卡罗方法，利用入射数据和从输电网络数据中计算的观测SI估计R(t)。 Results: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. Conclusions: We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2022/5/e34438 UR - https://doi.org/10.2196/34438 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486812 DO - 10.2196/34438 ID - info:doi/10.2196/34438 ER -