Defining Core Competencies and Training Priorities for Infectious Disease Dynamics as a Discipline
This study establishes the first structured framework of core competencies for doctoral-level expertise in infectious disease dynamics by utilizing an e-Delphi process to validate a consensus set of cross-cutting, applied modeling, and theory skills, thereby providing a standardized guide for curriculum development and trainee evaluation across the field.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
Imagine a field of science dedicated to understanding how diseases move through populations, not just by tracking who gets sick, but by building mathematical stories that explain why the sickness spreads and how it might be stopped. This field, known as infectious disease dynamics, has grown rapidly over the last twenty years, becoming a vital tool for public health officials. It sits at the intersection of biology, mathematics, and computer science, using complex calculations to simulate the behavior of viruses and bacteria. For a long time, the people who worked in this field arrived from many different backgrounds—some were mathematicians, others biologists or statisticians—and they learned their craft in unique ways, often guided by the specific interests of their mentors. Because there was no single map for this training, the skills and knowledge of new experts varied wildly from one university to another. As the world faced major health crises, the need for a consistent, high-quality understanding of these models became urgent, prompting a group of researchers to ask a fundamental question: what exactly should a scientist know to be an expert in this discipline?
To answer this, a team of seven researchers at the University of Utah set out to define the core skills required for doctoral-level training in infectious disease dynamics. They did not simply guess what was important; they built a framework through a careful, structured conversation with the wider scientific community. First, the team drafted a long list of potential skills, covering everything from general scientific habits to specific mathematical techniques. They then invited forty-three experts from around the world to review these ideas. These experts came from diverse sectors, including academia, government, and healthcare, and they represented different specialties within the field. The researchers asked these experts to rate each skill on a scale of importance, aiming for a strong agreement where at least seventy percent of the group felt a skill was essential.
The process involved two rounds of feedback, allowing the experts to refine their views and reach a consensus. In the end, the original list of dozens of proposed skills was trimmed down to a focused set of twenty-nine core competencies. These were organized into three main groups. The first group, called cross-cutting competencies, included seven skills that every expert agreed were necessary for everyone, regardless of their specific focus. These skills involve the ability to read and critique scientific literature, communicate complex ideas clearly through writing and visuals, and explain how their work applies to real-world health policies. The second group focused on applied modeling, containing ten skills for those who build models to solve immediate public health problems, such as forecasting outbreaks or guiding preparedness plans. The third group centered on theory, with twelve skills for those who develop the mathematical foundations of the models themselves, ensuring the underlying equations are sound and capable of solving complex problems.
The researchers found that the experts were very clear about what mattered most. The final list prioritized deep conceptual understanding and the ability to evaluate the assumptions behind a model over proficiency with specific software tools or professional mechanics like navigating the publication process. For instance, while the team initially suggested that trainees should know how to choose the right journal for their papers or manage project budgets, the experts decided these were not unique to the field and were not central to the core intellectual identity of the discipline. Similarly, while health equity is a critical topic, the experts did not agree on a single, rigid set of steps for teaching it. Instead, they concluded that the concept of equity is best taught through the general principle of understanding variation in populations, which is already woven into the core skills of building and testing models.
One notable outcome of the study was the decision to drop a specific track for data science. Although the team initially hoped to include a separate set of skills for data scientists, they could not gather enough experts in that specific area to reach a consensus. As a result, the final framework focuses on two main paths: applied modeling and theory. The researchers acknowledge that this framework is a starting point, not a finished product. It represents a shared agreement on what defines expertise in the field today, but they expect it to evolve as new methods and challenges arise. By making these expectations explicit, the study aims to help universities design better training programs, help students understand what they need to learn, and ensure that the scientists of tomorrow are prepared to tackle the complex, shifting landscape of infectious diseases with rigor and clarity.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.