2026-10-13T08:29:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34035851 2026-09-30T17:41:54Z category_1 category_15 category_21 category_734 category_931 category_132 portal_5 item_type_1 month_year_09_2026

<p>Methods behind Influpaint.</p> Joseph Lemaitre (25145381) Justin Lessler (116180) Biophysics Neuroscience Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Infectious Diseases somewhat overconfident projections leading ensemble methods cdc flusight challenges capture multimodal uncertainty achieves forecast accuracy 2024 &# 8211 2022 &# 8211 influpaint generates realistic encoding influenza seasons provide important information generative diffusion models diverse epidemic trajectories diffusion models influpaint learns influenza dynamics simulated trajectories epidemic dynamics time evaluation spatiotemporal images rich distribution retrospective evaluation partial observations hybrid dataset highly accurate flexible framework emergent trends disease dynamics current mechanistic conditional generation best performance <p><b>a.</b> Epidemic seasons are represented as images with time and space as axes, and pixel intensity corresponding to incident hospitalizations. <b>b.</b> Denoising Diffusion Probabilistic Models generate data by training a U-Net to restore an image that has been progressively corrupted by Gaussian noise. <b>c.</b> RePaint algorithm for forecasting. The ground truth and a mask (white: values to preserve; green: values to discard and replace by the generation process) are provided. At each denoising step, we stitch the noised ground truth and the intermediate denoised sample to obtain the updated trajectory.</p> 2026-09-30T18:00:00Z Image Figure 10.1371/journal.pcbi.1014846.g005 https://figshare.com/articles/figure/_p_Methods_behind_Influpaint_p_/34035851 CC BY 4.0