Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection.

Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.

Self-avoidant memory effects on enhanced diffusion in a stochastic model of environmentally responsive swimming droplets.

Enhanced diffusion is an emergent property of many experimental microswimmer systems that usually arises from a combination of ballistic motion with random reorientations. A subset of these systems, autophoretic droplet swimmers that move as a result of Marangoni stresses, have additionally been shown to respond to local, self-produced chemical gradients that can mediate self-avoidance or self-attraction. Via this mechanism, we present a mathematical model constructed to encode experimentally observed self-avoidant memory and numerically study the effect of this particular memory on the enhanced diffusion of such swimming droplets. To disentangle the enhanced diffusion due to the random reorientations from the enhanced diffusion due to the self-avoidant memory, we compare to the widely used active Brownian model. Paradoxically, we find that the enhanced diffusion is substantially suppressed by the self-avoidant memory relative to that predicted by only an equivalent reorientation persistence timescale in the active Brownian model. We attribute this to transient self-caging that we propose is novel for self-avoidant systems. Additionally, we further explore the model parameter space by computing emergent parameters that capture the velocity and reorientation persistence, thus finding a finite parameter domain in which enhanced diffusion is observable.