Abstract
Background Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.
Aim The dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.
We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.
Methods We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.
Results We reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.
Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.
Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.
In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important light to discuss hypothesis and design new experiments.
Competing Interest Statement
The authors have declared no competing interest.