From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling
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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 view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunesenescence.
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SciScore for 10.1101/2020.12.20.423670: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless the analysis conducted in the present work accounts for such limitations and the results obtained can be reasonably considered independent of such restrictions. Of course there are some cases reported in the …
SciScore for 10.1101/2020.12.20.423670: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless the analysis conducted in the present work accounts for such limitations and the results obtained can be reasonably considered independent of such restrictions. Of course there are some cases reported in the literature in which the course of infection has been extremely long, expecially in severly immunocompromised patients. However, the very complex and lenghty dynamics taking place in those cases are beyond the scope of this study, which instead represent at large the majority of observed cases. Finally, we should consider that the clinical ground of observation inevitably starts much later than in our model, as people ask for medical attention only after developing symptoms or after knowing of accidentally having been in contact with patients/carriers. Therefore the window of observation we consider in this paper is recapitulating more precisely the infection dynamics of the early days. Potential study directions should cover the duration of immunity, either natural or induced by the vaccine, including ways to indirectly verify it, the impact of immunesenescence on the elicited immunity or the combined effect of monoclonal antibody and vaccines in potential future terapies. Despite the extraordinary complexity of the immune system dynamics, the progress of simulation platforms suggests that a more intense interaction between clinicians and researchers in computational model could bring these models to the desidered quality for deployment in the medical field.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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