Alun L. Lloyd, North Carolina State University, Title: Bidirectional Contact Tracing for COVID-19. Abstract: Contact tracing is critical in controlling COVID-19 outbreaks in the absence of a vaccine, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we find that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in the effective reproduction number, R_{eff}, achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realized by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control. YouTube and Slides

Amanda Randles, Alfred Winborne Mordecai and Victoria Stover Mordecai Assistant Professor, Duke University. Title: Developing scalable, efficient, and accurate personalized flow simulations. Abstract: Patient-specific simulations are a promising area for personalized medicine and often times require efficient use of large-scale computational resources. In this talk, I will discuss two use cases: personalized blood flow modeling and air flow models to support ventilator splitting.  In each case, I will discuss how we have developed scalable models and tuned them to represent individual patients.  I will cover the acquisition of the data, building of the model, validation methods, and steps to ensure scalable and reproducible results.  In terms of the blood flow models, it has been shown that hemodynamic forces can play a key role in the localization and development of disease. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed, realistic model of human blood flow, however, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and the behavior of suspended bodies such as red blood cells. In this talk, I will discuss the development of HARVEY, a parallel fluid dynamics application designed to model hemodynamics in patient-specific geometries. I will cover the methods introduced to reduce the overall time-to-solution and enable near-linear strong scaling on some of the largest supercomputers in the world.  Finally, I will present the expansion of the scope of projects to address not only vascular diseases, but also treatment planning and the movement of circulating tumor cells in the bloodstream. For the ventilator splitting work, I will discuss how we established a new model, validated it, and were able to turn around a large parameter study using 800,000 compute hours over the course of one weekend.  In both cases, a problem-centric approach set the stage for building efficient models that can provide insight into patient-specific dynamics. YouTube and Slides