Contribution of Coronavirus-Specific Immunoglobulin G Responses to Complement Overactivation in Patients with Severe Coronavirus Disease 2019.

BACKGROUND: Excessive complement activation has been implicated in the pathogenesis of coronavirus disease 2019 (COVID-19), but the mechanisms leading to this response remain unclear. METHODS: We measured plasma levels of key complement markers, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and antibodies against SARS-CoV-2 and seasonal human common cold coronaviruses (CCCs) in hospitalized patients with COVID-19 of moderate (n = 18) and critical severity (n = 37) and in healthy controls (n = 10). RESULTS: We confirmed that complement activation is systemically increased in patients with COVID-19 and is associated with a worse disease outcome. We showed that plasma levels of C1q and circulating immune complexes were markedly increased in patients with severe COVID-19 and correlated with higher immunoglobulin (Ig) G titers, greater complement activation, and higher disease severity score. Additional analyses showed that the classical pathway was the main arm responsible for augmented complement activation in severe patients. In addition, we demonstrated that a rapid IgG response to SARS-CoV-2 and an anamnestic IgG response to the nucleoprotein of the CCCs were strongly correlated with circulating immune complex levels, complement activation, and disease severity. CONCLUSIONS: These findings indicate that early, nonneutralizing IgG responses may play a key role in complement overactivation in severe COVID-19. Our work underscores the urgent need to develop therapeutic strategies to modify complement overactivation in patients with COVID-19.

A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread.

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.