The core difficulty that Influpaint addresses is uncertainty that refuses to behave neatly. Traditional mechanistic models, built on equations describing transmission between susceptible and infected individuals, and classical statistical models fitted to historical patterns, both tend to produce forecasts that average over possible futures. Yet real influenza seasons are notoriously multimodal: a season might plausibly peak early and severe, or late and mild, and a forecast that hedges between these scenarios can end up predicting something that matches neither. When an unexpected variant emerges or holiday travel reshapes contact patterns, models anchored to past behavior often miss the turn entirely. Capturing this branching, lumpy uncertainty—many distinct plausible trajectories rather than one smoothed expectation—has been a persistent weakness of existing approaches.

Denoising diffusion models offer a different path. These models, which underpin many of today’s leading image generators, learn to create data by reversing a gradual noising process. During training, the model is shown examples that are progressively corrupted with random noise, and it learns to strip that noise away step by step. Once trained, the model can start from pure noise and iteratively refine it into a realistic sample drawn from the distribution it learned. Crucially, this generation can be made conditional: the model can be guided to produce samples consistent with partial observations, filling in the missing pieces much like an image-editing tool completes a partially masked photograph. Epidemiologists recognized that this inpainting capability maps naturally onto forecasting, where the observed recent past is known and the future is the region to be filled in.

Influpaint exploits that mapping with an elegant representational trick. Instead of treating influenza data as a table of weekly case counts by region, the researchers encode each influenza season as a spatiotemporal image. In these images, one axis represents geography—individual states or regions of the United States—while the other represents time, and the intensity of each pixel corresponds to influenza incidence in that place at that moment. A whole season’s epidemic, unfolding across the entire country, becomes a single picture. Patterns that are hard for conventional models to capture, such as a wave sweeping from the South toward the Northeast or synchronized peaks across distant states, become visual textures that a convolutional, image-oriented model can learn directly. The forecasting task then becomes literally an act of inpainting: given the left portion of the image, which shows the season so far, generate the right portion that completes it.

Training such a model requires a rich dataset, and here the team made a deliberate and consequential design choice. Surveillance data alone, however valuable, cover a limited number of seasons and may not span the full range of dynamics the model should learn to represent. To expand and diversify the training material, the researchers built a hybrid dataset combining real surveillance observations with simulated epidemic trajectories generated from models of influenza transmission. The simulations act as a kind of synthetic widening of experience, exposing the network to plausible epidemic shapes that history has not yet delivered. The composition of this mixture turned out to matter greatly: the best performance was achieved with a training set containing roughly 30 percent real surveillance data and 70 percent simulated trajectories, a balance that provided enough grounding in reality while leveraging the breadth of the synthetic data.

The evaluation of Influpaint proceeded in two stages, beginning with retrospective tests against historical data. In these experiments, the model was asked to generate forecasts as if it stood at various points during past seasons, and its outputs were scored against what actually happened. The results showed that Influpaint produces realistic and diverse epidemic trajectories, generating ensembles of possible futures that span the plausible outcome space rather than collapsing onto a single average path. Its forecast accuracy proved competitive with leading ensemble methods, the established top performers in influenza forecasting competitions. This is a notable result for a fundamentally new approach: rather than incrementally refining existing mechanistic or statistical machinery, the diffusion model matched them by learning the structure of epidemics directly from data.

The more demanding test came in real time. Influpaint was entered into the U.S. Centers for Disease Control and Prevention’s FluSight challenges during the 2022–2023, 2023–2024, and 2024–2025 influenza seasons, providing weekly forecasts alongside other teams’ models while the season was still unfolding. Performance improved substantially across the three seasons, reflecting both refinements to the model and the accumulating benefits of training data. The 2024–2025 season stood out: the model’s projections were highly accurate, capturing the course of the epidemic with a precision that few competitors matched. Yet the same season revealed a characteristic weakness. The forecasts, while accurate on average, were somewhat overconfident, meaning the model expressed more certainty in its predictions than the actual spread of outcomes warranted. In probabilistic forecasting, where decision-makers rely on the width of prediction intervals to gauge risk, overconfidence can be as damaging as inaccuracy.

That tension—sharp accuracy paired with miscalibrated confidence—illustrates both the promise and the open questions of generative epidemic forecasting. Diffusion models excel at learning rich distributions and producing samples that respect complex spatiotemporal structure, but ensuring that the spread of generated samples faithfully reflects true uncertainty requires careful calibration. The authors’ findings indicate that the framework is flexible and improvable: because forecasting is cast as conditional generation, the same machinery could in principle incorporate additional data streams, such as hospitalizations, virological reports, or behavioral indicators, simply by conditioning on more observed channels of the spatiotemporal image. The approach is not tied to influenza specifically, and the researchers present it as a general framework for probabilistic infectious disease forecasting rather than a single-purpose tool.

The broader significance of the work lies in what it says about the migration of generative AI into the sciences. Image-generation models succeeded because they learned the statistics of natural pictures without being told explicit rules of composition. Influpaint suggests that epidemic dynamics, when framed appropriately, have learnable statistical structure of a similar kind—waves that propagate, peaks that cluster, seasons that rhyme without repeating. For public health, the practical stakes are considerable: better-calibrated, spatially resolved forecasts could sharpen decisions about vaccine distribution and healthcare capacity during the critical weeks when an influenza season’s character is still being decided. For the modeling community, the study establishes a benchmark showing that generative approaches can stand shoulder to shoulder with the best ensemble methods, and points toward a research agenda in which simulated and observed data are deliberately blended to teach models about futures that have not yet happened. As diffusion models continue to mature, the picture of next winter’s flu season may arrive not as a single predicted curve, but as a gallery of plausible epidemics—each one painted by an algorithm that has learned, from data and simulation alike, how influenza seasons take shape.

Subject of Research: Generative diffusion models applied to spatiotemporal influenza forecasting

Article Title: Generative diffusion models for spatiotemporal influenza forecasting

Article References: Lemaitre, J., & Lessler, J. (2026). Generative diffusion models for spatiotemporal influenza forecasting. PLOS Computational Biology, 22(9), e1014846. https://doi.org/10.1371/journal.pcbi.1014846

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014846

Keywords: influenza, epidemic forecasting, diffusion models, machine learning, Influpaint, FluSight, CDC, spatiotemporal modeling, probabilistic forecasting, PLOS Computational Biology, public health, inpainting