Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
Hannover Medical School, Hannover, Germany.
JMIR Public Health Surveill. 2022 May 31;8(5):e34438. doi: 10.2196/34438.
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.
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.
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.
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.
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.
Surveillance Outbreak Response Management and Analysis System（SORMAS）包含一个管理模块，用于支持各国的疫情应对。它由病例、接触者和事件的文档记录、关联和跟踪组成。为了允许 SORMAS 用户实时可视化数据、计算基本监测指标和估计来自网络数据的流行病学参数，我们开发了 SORMAS Statistics（SORMAS-Stats）应用程序。
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，并举例说明 SORMAS-Stats 在应对 COVID-19 疫情中的应用。
基于快速审查和 SORMAS 用户请求的结果，我们在 SORMAS-Stats 中包括了以下可视化和参数估计：传播网络图、序列间隔（SI）、时变繁殖数 R(t)、分散参数 k 和以图形和表格形式呈现的其他监测指标。我们通过拟合正态分布、伽马分布和威布尔分布来估计 SI，观察感染者-感染者对的症状发作日期之间的天数分布。我们通过拟合负二项分布来估计 k，观察每个感染者的感染者数量。此外，我们应用马尔可夫链蒙特卡罗方法，并使用发病率数据和从传播网络数据计算得出的观察到的 SI 来估计 R(t)。
使用 2021 年 7 月 31 日至 10 月 29 日在法国勃艮第-弗朗什-孔泰地区报告的 COVID-19 接触者追踪数据，我们构建了一个包含 63570 个节点的网络图。该网络包括 1.75%（1115/63570）的事件、19.59%（12452/63570）的病例和 78.66%（50003/63570）的暴露者，包括 1238 对感染者-感染者和 3860 条传播链，其中 24.69%（953/3860）的传播链以事件作为索引感染者。与观察到的 SI 数据拟合最好的分布是对数正态分布，其平均值为 4.30（95%CI 4.09-4.51）天。我们估计的分散参数 k 为 21.11（95%CI 7.57-34.66），有效繁殖数 R 为 0.9（95%CI 0.58-0.60）。每周估计的 R(t) 值范围在 0.80 到 1.61 之间。
我们提供了一种实时估计流行病学参数的应用程序，这对于制定疫情应对策略至关重要。这些估计与先前研究的结果一致。SORMAS-Stats 应用程序可以通过提供广泛的可视化和监测指标的计算，极大地帮助使用 SORMAS 或类似工具的地区的公共卫生当局。
