Optimizing Multi-Dose Vaccination Protocols for Enhanced Viral Infection Defense

The effectiveness of vaccination in providing protection against viral infections hinges upon the quality and potency of the antibodies produced in response to the viral antigens. This production of antibodies is governed by a stochastic process known as affinity maturation, which involves multiple rounds of mutation and selection of the immune B cells. The neutralization capacity of the resulting antibodies is contingent upon various factors, including the dosage, timing, and composition of the antigens within the vaccines. Of particular importance, in the context of sequential vaccinations, is how the immune system's response is influenced by previous rounds of vaccination; this gives rise to important feedback effects such as modified antigen capture and epitope masking in subsequent vaccination rounds.

In this study, we present a computational model for antibody generation in response to multi-dose vaccination regimens, employing a stochastic simulation that encompasses the dynamics within both germinal and extra-germinal centers. Leveraging automatic differentiation techniques, we endeavor to optimize vaccination protocols designed to elicit an antibody response capable of conferring protective immunity against infections caused by potentially mutating viruses. Our research not only sheds light on the intricacies of vaccine optimization but also highlights the potential for a robust defense against important pathogens like SARS-CoV-2 and HIV through finely tailored vaccination strategies.