Mathematics

It is common in SIR models to assume that the infection rate is proportional to the product S*I of susceptible and infected individuals. This form is motivated by the law of mass action from chemistry. While this assumption works at the onset of the outbreak, it needs to be modified at higher rates such as seen currently in much of the world (as of June 2020). We propose a physics-based model which leads to a simple saturation formula based on first principles incorporating the spread radius and population density. We then apply this modified SIR model to coronavirus and show that it fits much better than the ``classical'' law of mass action.

Rapid development of a QSP model to support novel COVID-19 therapies. We intend to publish this model quickly to encourage community feedback. The simulated dynamics of immune response are modeled by describing viral activation of innate and adaptive immune processes involving both pro-inflammatory mediators regulating viral clearance and cell damage (e.g. neutrophils and cytotoxic lymphocytes) as well as counter-regulatory immune suppressive mediators (e.g. Treg cells and IL-10).

The primary distinction between severe and mild COVID-19 infections is the immune response. Disease severity and fatality has been observed to correlate with lymphopenia (low blood lymphocyte count) and increased levels of inflammatory cytokines and IL-6 (cytokine storm), damaging dysregulated macrophage responses, and T cell exhaustion due to limited recruitment. The exact mechanism driving the dynamics that ultimately result in severe COVID-19 manifestation remain unclear. Over the past two months, we have been working on developing tissue- and systemic-level models of the immune response to SARS-CoV-2 infection with the goal of pinpointing what may be causing dysregulated immune dynamics in severe cases. At the tissue level, we been working as part of an international collaboration to build a computational framework to study SARS-CoV-2 in the tissues. This platform is based upon PhysiCell, an open-source computational cell-based software. With this model, we have been investigating how the level of pro-inflammatory cytokines influence immune cell recruitment into the infected tissue and how this correlates with tissue damage. In parallel, we have constructed a systemic, within-host delay-differential equation model that accounts for the interactions between immune cell subsets, cytokines, lung tissue, and virus to help understand differential responses in COVID-19. While this work is still ongoing, this talk will address how a variety of mathematical and computational techniques contribute to the ongoing study of SARS-CoV-2 infections, helping to increase our understanding of COVID-19 severity.

* with Sofia Alfonso (McGill University), Rosemary Aogo (University of Tennessee Health Science Center), Courtney Davis (Pepperdine University), Amber M. Smith (University of Tennessee Health Science Center), Morgan Craig (Université de Montréal, CHU Sainte-Justine Research Centre)

We present an age stratified SEIR model of COVID 19 accounting for mitigated social contacts. With this model we explore a series of relaxation and return to normal scenarios, in terms of health system burden.

I will look at a simple theoretical model of a standard branching process with branchers removed by a contact tracing procedure. The talk will identify the parameter range in which the contact tracing is able to make the process sub- critical.