Scientific

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.

We show how a simple deterministic epidemic model without spatial structure can reproduce the evolution of confirmed Covid-19 case numbers in diverse countries and Brazilian states through use of a time-dependent contagion factor, beta(t). One expects that this function provides a link between the growth rate and mitigation policies. The model inserts a state A (presymptomatic) between states E (exposed) and I (infected) in the usual SEIR model, as well as distinguishing between confirmed and unconfirmed infected. With transition rates fixed at literature values, we vary the four free parameters in beta(t) to obtain a good description of time series of the cumulative number of confirmed cases. We then analyze the relation between changes in the contagion factor, as inferred from the time- series analysis, and mobility indexes based on cell-phone data.

Evolutionary epidemiological models illustrate how selection might act on SARS-CoV-2. Considering the limited data, selection favors increased transmission, longer pre-symptomatic periods, fewer asymptomatic cases, and lower disease severity. Viral mutations are expected to affect combinations of these traits, however, making it challenging to predict the direction and disease impact of evolution.