Abstract Despite intensive research since the emergence of SARS-CoV-2, it has remained unclear precisely which components of the early immune response protect against the development of severe COVID-19. Here, we perform a comprehensive immunogenetic and virologic analysis of nasopharyngeal and peripheral blood samples obtained during the acute phase of infection with SARS-CoV-2. We find that soluble and transcriptional markers of systemic inflammation peak during the first week after symptom onset and correlate directly with upper airways viral loads (UA-VLs), whereas the contemporaneous frequencies of circulating viral nucleocapsid (NC)-specific CD4 + and CD8 + T cells correlate inversely with various inflammatory markers and UA-VLs. In addition, we show that high frequencies of activated CD4 + and CD8 + T cells are present in acutely infected nasopharyngeal tissue, many of which express genes encoding various effector molecules, such as cytotoxic proteins and IFN-γ. The presence of IFNG mRNA-expressing CD4 + and CD8 + T cells in the infected epithelium is further linked with common patterns of gene expression among virus-susceptible target cells and better local control of SARS-CoV-2. Collectively, these results identify an immune correlate of protection against SARS-CoV-2, which could inform the development of more effective vaccines to combat the acute and chronic illnesses attributable to COVID-19.
Abstract Vaccine breakthrough infections with SARS-CoV-2 Omicron induced a higher level of protection compared to triple vaccination and contributed to herd immunity on a population level. To address the underlying immunological mechanisms, we studied the evolution of SARS-CoV-2-specific antibody and Tcell responses during vaccination and upon breakthrough infection in Bavarian residents between February 2021 and December 2022. Further, we investigated the temporal distance between completed vaccination and break-through infection, as well as any occurring re-infection. Each vaccination significantly increased peak neutralization titers against Wuhan, Delta, and Omicron BA.5 with simultaneous increases in circulating spike-specific Tcell frequencies. After vaccination, Omicron BA.5 neutralization titers were most significantly associated with a reduced hazard rate for SARS-CoV-2 infection, also when accounting for spikespecific Tcell responses. Yet, 97% of triple vaccinees became SARS-CoV-2 infected, often within a few months after their third vaccination. Breakthrough infections further boosted neutralization magnitude and breadth, broadened virusspecific Tcell responses to non-vaccine-encoded antigens and protected with an efficiency of 88% from further infections by December 2022. This effect was then assessed by utilizing mathematical modelling, which accounted for time-dependent infection risk in Bavaria, as well as the antibody and Tcell concentration at any time point after breakthrough infection. Our findings suggest that cross-variant protective hybrid immunity induced by vaccination and breakthrough infection was an important contributor to the reduced virus transmission observed in Bavaria in late 2022 and thereafter.
Summary Difference-in-Differences (DID) is a widely used tool for causal impact evaluation but is constrained by data privacy regulations when applied to sensitive personal information, such as individual-level performance records or healthcare data, that must not be shared with data analysts. Obtaining consent can reduce sample sizes or exclude treated/untreated groups, diminishing statistical power or making estimation impossible. Federated Learning, which shares aggregated statistics to ensure privacy, can address these concerns, but advanced federated DID software packages remain scarce. We derived and developed a federated version of the Callaway and Sant’Anna DID, implemented within the DataSHIELD platform. Our package adheres to DataSHIELD’s security measures and adds extra protections, enhancing data privacy and confidentiality. It reproduces point estimates, asymptotic standard errors, and bootstrapped standard errors equivalent to the non-federated implementation. We demonstrate this functionality on simulated data and real-world data from a malaria intervention in Mozambique. By leveraging federated estimates, we increase effective sample sizes leading to reduced estimation uncertainty, and enable estimation when single data owners cannot share the data but only have access to the treated or untreated group.
Abstract Despite intensive research since the emergence of SARS-CoV-2, it has remained unclear precisely which components of the early immune response protect against the development of severe COVID-19. To address this issue, we performed a comprehensive immunogenetic and virologic analysis of nasopharyngeal and peripheral blood samples obtained during the acute phase of infection with SARS-CoV-2. We found that soluble and transcriptional markers of systemic inflammation peaked during the first week after symptom onset and correlated directly with the upper airways viral loads (UA-VLs), whereas the contemporaneous frequencies of circulating viral nucleocapsid (NC)-specific CD4+ and CD8+ T cells correlated inversely with various inflammatory markers and UA-VLs. In addition, we observed high frequencies of activated CD4+ and CD8+ T cells in acutely infected nasopharyngeal tissue, many of which expressed genes encoding various effector molecules, such as cytotoxic proteins and IFN-γ. The presence of functionally active T cells in the infected epithelium was further linked with common patterns of gene expression among virus-susceptible target cells and better local control of SARS-CoV-2. Collectively, these results identified an immune correlate of protection against SARS-CoV-2, which could inform the development of more effective vaccines to combat the acute and chronic illnesses attributable to COVID-19.
Abstract Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side. We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.
Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side.We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)