Insights into B Cell and Antibody Kinetics Against SARS-CoV-2 Variants Using Mathematical Modelling (preprint)

B cells and antibodies are crucial in protecting against infections like SARS-CoV-2. However, antibody levels decline after infection or vaccination, reducing defences against future SARS-CoV-2 infections. To understand antibody production and decline, we developed a mathematical model that predicts germinal center B cell, long-lived plasma cell, memory B cell, and antibody dynamics. Our focus was on B cell activation and antibody generation following both primary and secondary SARS-CoV-2 infections. Aligning our model with clinical data, we adjusted antibody production rates for germinal center B cells and plasma B cells during primary and secondary infections. We also assessed antibody neutralization against Delta and Omicron variants post-primary and secondary exposure. Our findings showed reduced neutralization against Omicron due to its immune evasion. In primary and secondary exposures to Delta and Omicron, our predictions indicated enhanced antibody neutralization in the secondary response within a year of the primary response. We also explored waning immunity, demonstrating how B cell kinetics affect viral neutralization post-primary infection. This study enhances our understanding of humoral immunity to SARS-CoV-2 and can predict antibody dynamics post-infection or vaccination.

Pharmaceutical and Non-Pharmaceutical Interventions for Controlling the COVID-19 Pandemic (preprint)

Disease spread can be affected by pharmaceutical (such as vaccination) and non-pharmaceutical interventions (such as physical distancing, mask-wearing, and contact tracing). Understanding the relationship between disease dynamics and human behavior is a significant factor to controlling infections. In this work, we propose a compartmental epidemiological model for studying how the infection dynamics of COVID-19 evolves for people with different levels of social distancing, natural immunity, and vaccine-induced immunity. Our model recreates the transmission dynamics of COVID-19 in Ontario up to December 2021. Our results indicate that people change their behaviour based on the disease dynamics and mitigation measures. Specifically, they adapt more protective behaviour when the number of infections is high and social distancing measures are in effect, and they recommence their activities when vaccination coverage is high and relaxation measures are introduced. We demonstrate that waning of infection and vaccine-induced immunity are important for reproducing disease transmission in Fall 2021.

A Machine Learning Approach to Differentiate Between COVID-19 and Influenza Infection Using Synthetic Infection and Immune Response Data (preprint)

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC{approx}91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines (preprint)

During the SARS-CoV-2 global pandemic, several vaccines, including mRNA and ade-novirus vector approaches, have received emergency or full approval. However, supply chain logistics have hampered global vaccine delivery, which is impacting mass vaccination strategies. Recent studies have identified different strategies for vaccine dose administration so that supply constraints issues are diminished. These include increasing the time between consecutive doses in a two-dose vaccine regimen and reducing the dosage of the second dose. We consider both of these strategies in a mathematical modeling study of a non-replicating viral vector adenovirus vaccine in this work. We investigate the impact of different prime-boost strategies by quantifying their effects on immunological outcomes based on simple ordinary differential equations. The boost dose is administered either at a standard dose (SD) of 1000 or at a low dose (LD) of 500 or 250 vaccine particles. Simulated Second dose fractionation highlights previously shown dose-dependent features of the immune mechanism. In agreement with clinical characteristics of 175 COVID-19 recovered patients, the model predictions for either SD/SD or SD/LD regimens mainly show that by stretching the prime-boost interval until 18 or 20 weeks, the minimum promoted antibody (Nab) response is comparable with the neutralizing antibody level of COVID-19 recovered patients. The minimum stimulated antibody in SD/SD regimen is identical with the high level of clinical trial data. It is at the same range of the medium-high level of Nab in SD/LD, where the second dose is half or quarter of the standard dose.