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

TL;DR

This paper introduces a physics-informed neural network method that accurately infers regime changes in epidemiological data, such as lockdowns, from low-resolution COVID-19 death records at nearly daily precision.

Contribution

It presents a novel unsupervised, physics-informed CNN approach for deconvolving epidemiological data to detect regime changes without prior perturbation knowledge.

Findings

Achieves 0.93-day accuracy in identifying lockdowns.

Successfully applies to COVID-19 data over a year.

Provides a new methodology for high-resolution epidemiological analysis.

Abstract

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.