ABSTRACT
Summary Background Antiviral monoclonal antibodies (mAbs) developed for treatment of COVID-19 reduce the magnitude and duration of viral shedding and can thus potentially contribute to reducing transmission of the causative virus, severe acute respiratory coronavirus 2 (SARS-CoV-2). However, use of these mAbs in combination with a vaccine program has not been considered in public health strategic planning. Methods We developed an agent-based model to characterize SARS-CoV-2 transmission in the US population during an aggressive phase of the pandemic (October 2020 to April 2021), and simulated the effects on infections and mortality of combining mAbs as treatment and post-exposure prophylaxis (PEP) with a vaccine program plus non-pharmaceutical interventions. We also interrogated the impact of rapid diagnostic testing, increased mAb supply, and vaccine rollout. Findings Allocation of mAbs as PEP or targeting those ≥65 years provided the greatest incremental benefits relative to vaccine in averting infections and deaths, by up to 17% and 41%, respectively. Rapid testing, facilitating earlier diagnosis and mAb use, amplified these benefits. The model was sensitive to mAb supply; doubling supply further reduced infections and mortality, by up to two-fold, relative to vaccine. mAbs continued to provide incremental benefits even as proportion of the vaccinated population increased. Interpretation Use of anti-viral mAbs as treatment and PEP in combination with a vaccination program would substantially reduce SARS-CoV-2 transmission and pandemic burden. These results may help guide resource allocation and patient management decisions for COVID-19 and can also be used to inform public health policy for current and future pandemic preparedness. Funding Regeneron Pharmaceuticals.
Subject(s)
COVID-19ABSTRACT
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Waning antibody levels lead to reduced sensitivity of serological diagnostic tests over time. This undermines the utility of serological surveillance as the SARS-CoV-2 pandemic progresses into its second year. Here we develop a multiplex serological test for measuring antibodies of three isotypes (IgG, IgM, IgA) to five SARS-CoV-2 antigens (Spike (S), receptor binding domain (RBD), Nucleocapsid (N), Spike subunit 2, Membrane-Envelope fusion) and the Spike proteins of four seasonal coronaviruses. We measure antibody responses in several cohorts of French and Irish hospitalized patients and healthcare workers followed for up to eleven months after symptom onset. The data are analysed with a mathematical model of antibody kinetics to quantify the duration of antibody responses accounting for inter-individual variation. One year after symptoms, we estimate that 36% (95% range: 11%, 94%) of anti-S IgG remains, 31% (9%, 89%) anti-RBD IgG remains, and 7% (1%, 31%) anti-N IgG remains. Antibodies of the IgM isotype waned more rapidly, with 9% (2%, 32%) anti-RBD IgM remaining after one year. Antibodies of the IgA isotype also waned rapidly, with 10% (3%, 38%) anti-RBD IgA remaining after one year. Quantitative measurements of antibody responses were used to train machine learning algorithms for classification of previous infection and estimation of time since infection. The resulting diagnostic test classified previous infections with 99% specificity and 98% (95% confidence interval: 94%, 99%) sensitivity, with no evidence for declining sensitivity over the time scale considered. The diagnostic test also provided accurate classification of time since infection into intervals of 0 - 3 months, 3 - 6 months, and 6 - 12 months. Finally, we present a computational method for serological reconstruction of past SARS-CoV-2 transmission using the data from this test when applied to samples from a single cross-sectional sero-prevalence survey.