The Duke Clinical Research Institute (DCRI) and Duke School of Medicine have been deeply involved in developing network science and medical solutions throughout the covid-19 pandemic. From working closely with state governments and school systems to facilitate safe school reopening and operation via ABC Science Collaborative, to deploying home covid testing to underserved populations via Say Yes! Covid Test to co-leading the efforts to understand the long-term effects of covid-19 via Recover. In this talk we will examine the technology, process, privacy and security strategies that have enabled these initiatives.

Cancer is the second-leading cause of death in the United States. In addition, cancer is one of the most frequently diagnosed serious illnesses, with an estimated 16.9 million Americans with a history of an invasive malignancy alive as of January 1, 2019. Nearly 2 million invasive cancer cases were expected to be diagnosed in 2020. At the beginning of the COVID-19 pandemic, Advocate Aurora Health, Dana-Farber Cancer Institute, Fred Hutchinson Cancer Research Center, the University of Miami, and Vanderbilt University Medical Center (VUMC) led the formation of the COVID-19 and Cancer Consortium (CCC19), to rapidly collect and disseminate information about the especially vulnerable population of patients with active cancer and cancer survivors who are diagnosed with COVID-19. CCC19 started accruing data on March 17, 2020. As of September 2022, 123 institutions across North America have joined the consortium, with more than 15,000 cases reported to date. CCC19 maintains a registry to collect granular data about baseline characteristics, initial course of COVID-19, and longer-term outcomes.

We are currently at the beginning stages of a generation-defining revolution in biology. For the past two decades, breakthroughs in our understanding of genetics and genomics, coupled with those in AI and machine learning, have presented us with opportunities to radically improve healthcare around the world. Data is now a digital specimen, but as more and more data is collected, often in different formats and on disparate platforms, new solutions are needed to successfully integrate, store, compute, and secure data. This talk provides a short set of examples for how to handle large-scale medical studies in a secure and scalable fashion. It assesses contemporary realities, identifies potentially promising research directions, and investigates potential impact on the field of bioinformatics from a Computer Science perspective.