Department of Epidemiology, Helmholtz Centre for Infection Research Braunschweig, Germany; Institute for Epidemiology Social Medicine and Health Systems Research, Hannover Medical School (MHH) Hannover, Germany.
Institute of Epidemiology and Social Medicine, University of Münster, Germany.
Int J Infect Dis. 2024 Feb;139:50-58. doi: 10.1016/j.ijid.2023.11.014. Epub 2023 Nov 24.
Throughout the SARS-CoV-2 pandemic, Germany like other countries lacked adaptive population-based panels to monitor the spread of epidemic diseases.
To fill a gap in population-based estimates needed for winter 2022/23 we resampled in the German SARS-CoV-2 cohort study MuSPAD in mid-2022, including characterization of systemic cellular and humoral immune responses by interferon-γ-release assay (IGRA) and CLIA/IVN assay. We were able to confirm categorization of our study population into four groups with differing protection levels against severe COVID-19 courses based on literature synthesis. Using these estimates, we assessed potential healthcare burden for winter 2022/23 in different scenarios with varying assumptions on transmissibility, pathogenicity, new variants, and vaccine booster campaigns in ordinary differential equation models.
We included 9921 participants from eight German regions. While 85% of individuals were located in one of the two highest protection categories, hospitalization estimates from scenario modeling were highly dependent on viral variant characteristics ranging from 30-300% compared to the 02/2021 peak. Our results were openly communicated and published to an epidemic panel network and a newly established modeling network.
We demonstrate feasibility of a rapid epidemic panel to provide complex immune protection levels for inclusion in dynamic disease burden modeling scenarios.
在整个 SARS-CoV-2 大流行期间，德国与其他国家一样，缺乏基于人群的适应性面板来监测传染病的传播。
为了填补 2022/23 年冬季基于人群的估计值的空白，我们在 2022 年年中重新抽样了德国 SARS-CoV-2 队列研究 MuSPAD，包括通过干扰素-γ释放试验（IGRA）和 CLIA/IVN 试验对系统细胞和体液免疫反应进行特征描述。我们能够根据文献综述将我们的研究人群分类为四个具有不同保护水平的组，以防止严重 COVID-19 病程。使用这些估计值，我们使用不同的传输率、致病性、新变体和普通微分方程模型中的常规疫苗加强运动假设，在不同场景下评估 2022/23 年冬季的潜在医疗保健负担。
我们从德国的八个地区纳入了 9921 名参与者。虽然 85%的个体位于两个最高保护类别之一，但情景建模中的住院估计值高度依赖于病毒变异特征，与 2021 年 02 月的峰值相比，差异在 30%至 300%之间。我们的结果公开交流并发布给了一个流行疾病小组网络和一个新成立的建模网络。
我们证明了快速流行疾病面板的可行性，该面板可提供复杂的免疫保护水平，以纳入动态疾病负担建模场景。
