ArticleScience advances2023
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Actionable Forecasting as a Determinant of Biological Adaptation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Reconstruction of excitation waves from mechanical deformation using physics-informed neural networks.Scientific reports · 2024Article
- Rapid deep learning-assisted predictive diagnostics for point-of-care testing.Nature communications · 2024Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The ability to infer the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is crucial for multiple applications. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions (NPIs) with 0.93-day accuracy over the time span of a year.
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