A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.

Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.

Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.

INTRODUCTION: Data on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race and ethnic disparities in the SARS-CoV-2 pandemic. METHODS: This is a cross-sectional analysis of the COVID-19 Surveillance and Outcomes Registry, which captures data for a large healthcare system, comprising one central tertiary care hospital, seven large community hospitals and an expansive ambulatory/emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analysed sociodemographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (Charlson Comorbidity Index, hypertension, diabetes, obesity) factors. Multivariable logistic regression models were fitted to provide adjusted OR (aOR) and 95% CI for likelihood of a positive SARS-CoV-2 test. Structural equation modelling (SEM) framework was used to explore three mediation pathways (low income, high population density, high comorbidity burden) for the association between non-Hispanic black (NHB) race, Hispanic ethnicity and SARS-CoV-2 infection. RESULTS: Among 20 228 tested individuals, 1551 (7.7%) tested positive. The overall mean (SD) age was 51.1 (19.0) years, 62% were females, 22% were black and 18% were Hispanic. NHB and Hispanic ethnicity were associated with lower socioeconomic status and higher population density residence. In the fully adjusted model, NHB (vs non-Hispanic white; aOR, 2.23, CI 1.90 to 2.60) and Hispanic ethnicity (vs non-Hispanic; aOR, 1.95, CI 1.72 to 2.20) had a higher likelihood of infection. Older individuals and males were also at higher risk of infection. The SEM framework demonstrated a significant indirect effect of NHB and Hispanic ethnicity on SARS-CoV-2 infection mediated via a pathway including residence in densely populated zip code. CONCLUSIONS: There is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic that are potentially mediated through unique social determinants of health.