Initiatives

Estimating infection levels in real-time is crucial for understanding epidemic severity and guiding mitigation efforts, including distancing and vaccination policies. While past studies have addressed testing biases and errors, inferring disease prevalence through patient testing remains challenging. This is due to inaccuracies, biases, variations in test availability, and changes in both disease severity and the public’s attitude towards testing.
 
The minisymposium “From Data to Diagnosis: Advancements in Prevalence Estimation and Testing’ focuses on these and related topics at the intersection of applied mathematics, epidemiology, and medicine.

Across multiple domains of science, engineering, and medicine, excitement is growing about the potential of digital twins to transform scientific research, industrial practices, and many aspects of daily life. A digital twin couples computational models with a physical counterpart to create a system that is dynamically updated through bidirectional data flows as conditions change. Going beyond traditional simulation and modeling, digital twins could enable improved medical decision-making at the individual patient level, predictions of future weather and climate conditions over longer timescales, and safer, more efficient engineering processes. However, many challenges remain before these applications can be realized.
 
This report identifies the foundational research and resources needed to support the development of digital twin technologies. The report presents critical future research priorities and an interdisciplinary research agenda for the field, including how federal agencies and researchers across domains can best collaborate.

The digital twin is an emerging technology that builds on the convergence of computer science, mathematics, and the life sciences. Digital twins have the potential to open up new capabilities across biomedical research, with applications ranging from personalized medicine to pharmaceutical development to clinical trials.

The National Academies invite you to join us for a workshop on the use of digital twins for biomedical research on Monday, January 30, 2023 from 10am to 4:30pm ET. During the workshop, speakers and participants will discuss the definition of a digital twin within the context of biomedical research and identify current methods for their development and use.

Workshop panels will address digital twins across different scales, including digital twins at the cellular, organ, whole human, and population levels. Panelists will discuss key technical challenges and opportunities for scalable digital twins, such as uncertainty quantification, data visualization, and privacy and ethics considerations. The workshop will also explore connections to fields outside the biomedical domain.
This event is one of three input gathering workshops organized as part of a larger National Academies’ study on research gaps and future directions for digital twins.

In this workshop, we will assemble a group of 20-30 scientists with expertise in immunology, clinical science, and mathematical and computational modeling, to discuss the main challenges facing the development of digital twin technology, determine the most promising immediate application areas, outline a conceptual map of an immune system model appropriate for these application areas, and determine the key data needs to calibrate, validate, and personalize such models.

The workshop Building Immune Digital Twins aims to make immune digital twin technology a reality. Over the course of three weeks, it will bring together researchers from each areas for activities ranging from extended active team work on specific immune digital twin projects to lectures, discussion and working groups, and brainstorming sessions for new projects and applications. Over the course of the program, participants will develop a network of collaborators and experts in all relevant areas of research. The ultimate goal of the workshop is to help create a long-term interdisciplinary immune digital twin community.

Project Objective: Develop specific guidelines for the quantitative biology community to help counter the most dire threats to human health we will face over the course of the next 30 years. These guidelines will be based on an assessment by the diverse communities of domain experts, such as clinicians, population health experts, health-related businesses, and others. They are intended to inform the quantitative biology community, and serve as the basis for blueprints of research initiatives, funding plans, training programs, or simply as a guide for new students and researchers in the quantitative biology field who are developing their own research programs.

Focus and structure of the working group: The community of modelers developing epidemiological and population-scale models is already extensive and well-integrated, in part due to the NIGMS MIDAS program. Within-host modeling of viral pathogens is much more limited. Therefore, the working group will initially focus on within-host scales, in particular the complex interactions between viral infection, host physiology, and the immune system. A main long-term deliverable of the working group will be an overall strategy for a coordinated multi-scale modeling effort which becomes a customizable translational technological platform for rapidly creating improved personalized prognoses and therapies in response to emerging viral pandemics. It will also include a plan on how to mobilize and coordinate the modeling community to support this effort.

Topological data analysis (TDA) relies heavily on a handful of C++ libraries developed primarily by and for statistical topologists. As TDA matures and standard workflows emerge, more accessible and modular implementations become necessary. The tdaverse is a collection of R packages designed to integrate TDA functionality into the tidyverse–tidymodels ecosystem in R.

The purpose of the work is to develop a multiscale computational model to simulate the invasion of the human lungs by spores of the opportunistic fungus Aspergillus fumigatus. In particular, we explore the role of nutritional immunity in clearing infection, with a focus on iron. This project is currently funded by The National Institutes of Health and The National Science Foundation of the United States of America. The team includes members from UF Health and Kitware.