Mapping knowledge user participation approaches in infectious disease transmission modeling (participatory modeling): a scoping review
This scoping review of 156 infectious disease modeling studies reveals that while knowledge user participation is common, it is predominantly limited to downstream tasks and policymakers, highlighting a need for more equitable, early engagement of diverse stakeholders to strengthen model relevance and uptake.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
When a new disease begins to spread, public health officials often turn to mathematical models to understand how it moves through a population and to decide which interventions might stop it. These models are not crystal balls that predict the future with perfect certainty; rather, they are simplified representations of reality, built by researchers to simulate how infections pass from one person to another. Because these simulations can shape major decisions—such as when to close schools, how to distribute vaccines, or where to send medical teams—the people who use the models need to trust them. For a long time, the process of building these models was largely hidden inside academic institutions, with experts making the choices about what to include and what to leave out. However, there is a growing recognition that the people most affected by these diseases, as well as the officials who must act on the findings, should have a seat at the table while the models are being built. This approach, known as participatory modeling, invites those outside the laboratory to help shape the questions being asked and the answers being sought.
A team of researchers recently set out to understand how this collaboration is actually happening in the real world. They conducted a massive review of nearly ten thousand scientific studies published over twenty-four years, looking specifically for instances where infectious disease models were created with the help of outside partners. After carefully filtering through the literature, they identified 156 studies where such collaboration was reported. These studies covered 21 different infectious diseases and took place in 98 countries, with a significant portion focusing on the recent COVID-19 pandemic, followed by HIV and tuberculosis. The researchers found that while the practice of involving outside voices is becoming more common, the way it happens is often uneven. In the vast majority of cases, the people invited to help were government officials or program managers who make policy decisions. In contrast, community groups and individuals who have personally lived through the disease were involved in fewer than one in five of the studies.
The review revealed a distinct pattern in how these collaborations unfold. Most of the time, outside partners were brought in only after the heavy lifting of the model had already been designed. They were frequently asked to provide data, such as local infection rates or contact patterns, or to help interpret the final results once the model was finished. This is akin to asking a local guide to help navigate a map only after the route has already been drawn by someone who has never visited the territory. The researchers found that fewer than half of the studies involved these partners in the earliest and most critical stages, such as deciding what questions the model should answer or determining the basic structure of how the disease spreads. Even fewer were involved in checking the model's internal logic or testing its assumptions. This suggests that while the models are becoming more connected to the real world, the power to define the fundamental shape of the model usually remains with the academic experts.
To make sense of these different ways of working together, the researchers identified five distinct approaches. The most common was a "commissioning" style, where a government agency hires a research team to answer a specific question, with the agency defining the problem and reviewing the final answer. Another approach involved a "third-party" intermediary, such as an international organization, that connects funders with researchers. A third method, called "plug-and-play," allows users to interact with a pre-built model, exploring different scenarios on a dashboard without changing how the model works underneath. A fourth approach, "crowdsourcing," gathers data directly from the public, such as symptom reports, to feed into the model, but without giving those contributors a say in the model's design. The fifth and least common approach was "active co-production," where community members and researchers worked together from the very beginning to build the model, sharing the power to make decisions about everything from the research questions to the final conclusions.
The study concludes that while the field is moving toward more inclusive practices, there is still a long way to go to ensure that the models truly reflect the needs and realities of the communities they serve. The researchers suggest that for these tools to be most effective and trusted, the involvement of diverse voices needs to happen earlier in the process, before the model is built, rather than just at the end. They also note that the current reporting of these collaborations is often inconsistent, making it difficult to know exactly how much influence different groups have. By establishing clearer guidelines for how to conduct and report these partnerships, the scientific community can help ensure that the models used to guide public health decisions are not just mathematically sound, but also socially relevant and equitable.
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