TransCode: Uncovering COVID-19 transmission patterns via deep learning.

Background: The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

Methods: We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.

Commonality and Individuality-Based Subspace Learning.

Subspace learning (SL) plays a key role in various learning tasks, especially those with a huge feature space. When processing multiple high-dimensional learning tasks simultaneously, it is of great importance to make use of the subspace extracted from some tasks to help learn others, so that the learning performance of all tasks can be enhanced together. To achieve this goal, it is crucial to answer the following question: How can the commonality among different learning tasks and, of equal importance, the individuality of each single learning task, be characterized and extracted from the given datasets, so as to benefit the subsequent learning, for example, classification? Existing multitask SL methods usually focused on the commonality among the given tasks, while neglecting the individuality of the learning tasks.

Optimal resource allocation with spatiotemporal transmission discovery for effective disease control.

Background: The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks?

Methods: We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant.