@文章{信息:doi/10.2196/34438，作者="Silenou, Bernard C和Verset, Carolin和Kaburi, Basil B和Leuci, Olivier和Ghozzi, St{\'e}phane和Duboudin, C{\'e}dric和Krause, G{\'e}rard"，标题="一种利用接触者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"，期刊="JMIR公共卫生监测"，年="2022"，月="5"，日="31"，卷="8"，数="5"，页="e34438"，关键词="COVID-19;疾病爆发;接触者追踪;连续区间;基本繁殖数;传染病潜伏期;superspreading事件;远程医疗;公共卫生;流行病学; surveillance tool; outbreak response; pandemic; digital health application; response strategy", abstract="Background: The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. Objective: This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. Methods: Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. Results: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comt{\'e} 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. ", issn="2369-2960", doi="10.2196/34438", url="https://publichealth.www.mybigtv.com/2022/5/e34438", url="https://doi.org/10.2196/34438", url="http://www.ncbi.nlm.nih.gov/pubmed/35486812" }