ABSTRACT
Despite the need to generate valid and reliable estimates of protection against SARS-CoV-2 infection and severe course of COVID-19 for the German population in summer 2022, there was a lack of systematically collected population-based data allowing for the assessment of the protection level in real-time. In the IMMUNEBRIDGE project, we harmonised data and biosamples for nine population-/hospital-based studies (total number of participants n=33,637) to provide estimates for protection levels against SARS-CoV-2 infection and severe COVID-19 between June and November 2022. Based on evidence synthesis, we formed a combined endpoint of protection levels based on the number of self-reported infections/vaccinations in combination with nucleocapsid/spike antibody responses ("confirmed exposures"). Four confirmed exposures represented the highest protection level, and no exposure represented the lowest. Most participants were seropositive against the spike antigen; 37% of the participants [≥]79 years had less than four confirmed exposures (highest level of protection) and 5% less than three. In the subgroup of participants with comorbidities, 46-56% had less than four confirmed exposures. We found major heterogeneity across federal states, with 4%-28% of participants having less than three confirmed exposures. Using serological analyses, literature synthesis and infection dynamics during the survey period, we observed moderate to high levels of protection against severe COVID-19, whereas the protection against SARS-CoV-2 infection was low across all age groups. We found relevant protection gaps in the oldest age group and amongst individuals with comorbidities, indicating a need for additional protective measures in these groups.
Subject(s)
COVID-19ABSTRACT
Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many contact surveys have been conducted to measure changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. These surveys were typically conducted longitudinally, using protocols that differ from those used in the pre-pandemic era. We present a model-based statistical approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model incorporates computationally efficient Hilbert Space Gaussian process priors to infer the dynamics in age- and gender-structured social contacts and is designed to adjust for reporting fatigue in longitudinal surveys. We demonstrate on simulations the ability to reconstruct contact patterns by gender and 1-year age interval from coarse data with adequate accuracy and within a fully Bayesian framework to quantify uncertainty. We investigate the patterns of social contact data collected in Germany from April to June 2020 across five longitudinal survey waves. We reconstruct the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contacts rebounded in a structured, non-homogeneous manner. We also show that by July 2020, social contact intensities remained well below pre-pandemic values despite a considerable easing of non-pharmaceutical interventions. This model-based inference approach is open access, computationally tractable enabling full Bayesian uncertainty quantification, and readily applicable to contemporary survey data as long as the exact age of survey participants is reported.
ABSTRACT
BackgroundA considerable proportion of SARS-CoV-2 transmission occurs from asymptomatic and pre-symptomatic cases. Therefore, different polymerase chain reaction (PCR)- or rapid antigen test (RAT)-based approaches are being discussed and applied to identify infectious cases that would have gone undetected (e.g., in nursing homes). In this article, we provide a framework to estimate the time-dependent risk of being infectious after a negative SARS-CoV-2 test and we simulate the number of expected cases over time in populations of individuals who initially tested negative. MethodsA Monte Carlo approach is used to simulate infections that occurred over a one-week period in populations with 1,000 individuals following a negative SARS-Cov-2 test. Parameters representing the application of PCR tests or RATs are utilized, and SARS-CoV-2 7-day incidences between 25 and 200 per 100,000 people are considered. Simulation results are compared to case numbers predicted via a mathematical equation. ResultsThe simulations showed a linear increase in cases over time in populations of individuals who initially tested SARS-CoV-2 negative. The different false negative rates of PCR tests and RATs have a strong impact on the number of simulated cases. The simulated and the mathematically predicted case numbers were comparable. However, Monte Carlo simulations highlight that, due to random effects, infectious cases can exceed predicted case numbers even shortly after a test was conducted. ConclusionsThe analysis demonstrates that the number of infectious cases in a population can be effectively reduced by the screening of asymptomatic individuals. However, the time since the negative test and the underlying SARS-CoV-2 incidence are critical parameters in determining the observed subsequent number of cases in tested populations.