Linig Xu and Yi Jiang Video

Ellen Kuhl Video

Linig Xu and Yi Jiang Abstract: As we breathe, small particles ranging from aerosolized droplets to pollutants enter our airways and are trapped by the thin layer of mucus that coat the airway surface. Mucociliary clearance is the first line of defense of our respiratory system against these potential pathogens and allergens. Periodic beating of cilia from the ciliated cells in the airway epithelium drive the mucus flow and eventually transport these particles out of the airway. We investigate the mixing and transport of these particles using direct 3D numerical simulations coupling the cilia beating and mucus flow. Extending from previous models of single cilium and small clusters of cilia, we model clusters of ciliated cells at a length scale comparable to tissue experiments. Our simulations discover an optimal cilia cluster spacing for efficient transport, and that asynchronous cilia beating (metachrony) tends to inhibit transport and increase mixing. Most relevant to infectious disease is when the pathogens kill the ciliated cells, the decreased local cilia density could significantly reduce the directed particle transport, and effectively increase local pathogen density. The model also shows the spatiotemporal inhomogeneity in particle diffusion: short distance diffusion and long distance clustering, as well as short-time simple diffusion and longer-timescale sub-diffusion and super-diffusion.  These results can inform the spatial models of virus replication and viral-host interaction.

Ellen Kuhl Abstract: Understanding the outbreak dynamics of COVID-19 through the lens of data-driven modeling is an elusive but significant goal. Within the past year, the COVID-19 pandemic has resulted in more than 100 million reported cases and more than 2.5 million deaths worldwide. Unlike any other disease in history, COVID-19 has generated a massive amount of data, well documented, continuously updated, and broadly available. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from one year of COVID-19 modeling. We highlight the early success of classical infectious disease models and show why these models fail to predict the current dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data to make informed predictions and guide political decision making. We anticipate that this presentation will stimulate discussion within the IMAG community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic.