# Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

arXiv: https://arxiv.org/abs/2209.07802 · HTML: https://mlsanity.com/paper/2209.07802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepXDE: A deep learning library for solving differential equations](https://mlsanity.com/paper/1907.04502/) (2019)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)

## Built on by (most cited first, 2 of 2)

- [Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization](https://mlsanity.com/paper/2602.19775/) (2026)
- [Propensity Straight-Through Gradients for Discrete Stochastic Systems](https://mlsanity.com/paper/2608.25631/) (2026)

## Abstract (arXiv)

Inferring the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is as relevant as challenging. It is a requirement for current approaches to overcome the need to know the details of the perturbations to proceed with the analyses. 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 with 0.93-day accuracy over the time span of a year.