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Abstract Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as “proof of concept” regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality. 

In the early 2000s, our primarily undergraduate, white institution (PUI/PWI), began recruiting and enrolling higher numbers of students of color and first-generation college students. However, like many of our peer institutions, our established pedagogies and mindsets did not provide these students an educational experience to enable them to persist and thrive in STEM. Realizing the need to systematically address our lack of inclusivity in science majors, in 2012 faculty from multiple disciplines developed the Science, Math, and Research Training (SMART) program. Here, we describe an educational innovation, originally funded by a grant from the Howard Hughes Medical Institute, designed to support and retain students of color, first generation college students, and other students with marginalized identities in the sciences through a cohort-based, integrated, and inclusive first-year experience focused on community and sense of belonging. The SMART program engages first-year students with semester-long themed courses around “real world” problems of antibiotic resistance and viral infections while integrating the fields of Biology, Chemistry, Mathematics, and an optional Computer Science component. In the decade since its inception, 97% of SMART students have graduated or are on track to graduate, with 80.9% of these students earning a major in a STEM discipline. Here, we present additional student outcomes since the initiation of this program, results of the student self-evaluative surveys SALG and CURE, and lessons we have learned from a decade of this educational experience. 

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.