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

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.

Rapid deep learning-assisted predictive diagnostics for point-of-care testing

“…Previous studies have successfully achieved shorter histopathology tissue staining times using generative adversarial network (GAN)-based virtual staining 40 , 41 and applied deep learning methodologies to enhance efficiency in plaque assays 42 . Moreover, the utilization of long short term memory (LSTM) deep learning algorithms has expedited polymerase chain reaction (PCR) analysis 43 , enabled the prediction of infections based on time-series data from affected individuals 44 , and facilitated the utilization of longitudinal MRI images for predicting treatment responses 45 . Meanwhile, the demand for diagnostic tools achieving shortened assay time and maintained sensitivity remains high, but few studies address for achieving AI-assisted fast assay, especially for POCT.…”