Government actuaries and public health specialists have tested a way to represent uncertainty in vaccine development as part of pandemic planning, linking possible development timelines to potential economic consequences. The work is a hypothetical proof of concept, not an operational forecast or advice on which response policymakers should choose.
The Government Actuary’s Department worked with the UK Health Security Agency on a simplified model intended to show how actuarial techniques might complement scientific and economic analysis. Its focus was not to predict when a future vaccine would be ready. Instead, the team explored how uncertain steps in development, and the number of candidates being considered, could shape a range of possible outcomes.
From milestones to a range of outcomes
Vaccine development involves a sequence of milestones, and the timing of each is uncertain. The model represented those milestones and tested many plausible pathways through them, drawing on expert input and desk-based research. It then connected the resulting timescales with a measure of potential economic impact associated with pandemic-response choices.
Rather than returning one forecast, the analysis produced a distribution of outcomes. That allows a decisionmaker to distinguish a central estimate from less likely but more severe possibilities and to examine which assumptions have the greatest influence. In practical terms, the approach is designed to make uncertainty visible instead of hiding it behind a single headline figure.
The number of vaccine candidates mattered in the scenarios explored. The analysis highlighted that relying on fewer candidates could increase expected development times and, in turn, potentially raise economic costs. This is a concentration risk: when a plan depends heavily on a small number of routes succeeding, setbacks in those routes can have a larger effect on the overall picture.
The model’s value, as described by the teams, lies in providing a consistent framework for comparing options and testing how results change when assumptions or evidence change. It can help organise questions about trade-offs, rather than settle them. Scientific evidence remains essential to judging whether candidates are promising, while operational and policy decisions require considerations beyond the model’s economic lens.
A framework still requiring further work
The project does not establish that a particular vaccine portfolio would be optimal, quantify a real-world probability of success, or show what a future pandemic would cost. No numerical forecast or recommendation is presented. Its simplified assumptions and hypothetical pathways are there to demonstrate a method, not to stand in for the complexities of an actual emergency.
The work is explicitly non-advisory. Further research, validation and assurance would be needed before adapting the model for live decisions. A future version could be updated as evidence accumulates, including observed development times, changes in the number of candidates and revised economic assumptions. Such updates could alter the range of outcomes; they would not remove the uncertainty inherent in a fast-moving health emergency.
That distinction matters because models can make a complicated risk easier to discuss while still depending on choices about what to include and how to represent it. A distribution is not a guarantee that every outcome has been captured. The proof of concept instead offers a way to ask more structured questions: which assumptions drive the results, how sensitive are comparisons to new evidence, and where might reliance on a narrow set of candidates leave decisionmakers exposed?
For pandemic preparedness, the central contribution is therefore methodological. Actuarial analysis can connect scientific uncertainty to possible economic consequences and help communicate a range of scenarios. The UKHSA and GAD work demonstrates that potential, while its own caveats make clear that a demonstration model is not yet a tool for directing a live response.
