Identifying the Superspreader in Proactive Backward Contact Tracing by Deep Learning

The goal of proactive contact tracing is to diminish the spread of an epidemic by means of contact tracing mobile apps and big data analysis. Finding superspreaders as has been used in Japan and Australia during the early days of the COVID-19 pandemic has proven effective as backward contact tracing can pick up infections that might otherwise be missed. In this paper, we formulate a proactive contact tracing problem to identify the superspreaders using maximum-likelihood estimation, graph traversal and deep learning algorithms. This problem is challenging due to its sheer combinatorial complexity, problem scale and the fact that the underlying infection network topology is rarely known. We propose a deep learning-based framework using Graph Neural Networks to iteratively refine the supervised learning of proactive contact tracing networks using smaller infection networks and to identify the superspreader. By optimizing the graph traversal and topological features for deep learning, proactive contact tracing strategies can be developed to contain superspreading in an epidemic outbreak.

Detection and Diagnosis Technologies for SARS-CoV-2

Detection and diagnosis platforms play key roles in early warning, outbreak control and exit strategy for any pandemic, and they are especially pertinent for the Coronavirus disease 2019 (COVID-19) pandemic. The challenges posed by the speed and extent of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) spread around the globe also offered unprecedented opportunities for the development and deployment of novel strategies and products - not only vaccines and therapeutics, but also diagnostics. This chapter provides a brief summary of the vast array of molecular, serological, cell-based and other diagnostic tools for the specific detection of SARS-CoV-2 infections and immune responses. The focus is on the principles and applications of each platform, while detailed protocols can be found in the cited references. © 2023 by World Scientific Publishing Co. Pte. Ltd.

Deep Single-Cell, Proteogenomic Insights from Sars-Cov-2 Infected Lung Tissues

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19) and is likely to lead to complexities in treating thoracic malignancies. Patients with lung cancer are at an increased risk of becoming infected with the SARS-CoV-2 virus and experience higher morbidity and mortality than the general population. However, little is known about the host tissue and cellular responses associated with SARS-CoV-2 infection, symptoms, and disease severity. Methods Here, we use the Nanostring GeoMX Digital Spatial Profiler (DSP) and CoxMX Spatial Molecular Imager (SMI) technology to determine tissue signatures, and spatially resolved quantitative single-cell proteogenomic changes driven by SARS-CoV-2 infection. This dual approach was used to generate an in-depth picture of the pumonary transcriptional and proteomic landscape of COVID-19, pandemic H1N1 and uninfected control patients.1 Rapid autopsy COVID-19 lung samples were collected across two independent cohorts of patients, and tissue microarrays (TMAs) were prepared. For GeoMx, n=10 COVID-19, n=10 pH1N1 and n=5 normal control tissues were compared. For CosMx, n=19 COVID-19 cores in technical replicates, and n=20 normal control tissues were compared. Tissue-based gene signatures were subsequently tested in the peripheral samples from COVID-19 patients. Results SARS-CoV-2 viral presence was confirmed by RNAscope and integrated to inform region of interest and cell types involved in infection. Analysis of the Nanostring GeoMx data revealed tissue signatures associated with SARS-CoV-2 infection, including Type 1 IFN, blood coagulation, hypoxia and angiogenesis. Analysis of the Nanostring CosMx data enabled single cell typing and mapping of tissue-specific signatures to cellular compartments of interest (e.g. macrophages, fibroblasts) and investigation of complex cell population heterogeneity and interactions. All these while preserving spatial context and highlighted differential cell type distribution in the lungs of COVID-19 patients compared to non-infected controls. Our tissue-based Type 1 IFN signatures, when tested in the blood, were found to be predictive of disease severity in COVID-19 patients when measured within the first few days of symptom onset. Conclusions Here, we've used innovative, cutting-edge spatial transcriptomics approaches to delineate tissue signatures and cellular profiles unique to COVID-19 and common across acute respiratory distress syndrome. These data will aid in understanding the proteogenomic landscape of SARS-CoV-2 infected lung tissues and form new knowledge for the impact on thoracic malignancies, and treatments such as immunotherapy. Moreover, the study demonstrates how tissue-based findings can be rapidly developed into signatures tested in noninvasive samples.

An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods,data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.

Human Nasal Epithelial Cells Sustain Persistent SARS-CoV-2 Infection In Vitro, despite Eliciting a Prolonged Antiviral Response

The dynamics of SARS-CoV-2 infection in COVID-19 patients are highly variable, with a subset of patients demonstrating prolonged virus shedding, which poses a significant challenge for disease management and transmission control. In this study, the long-term dynamics of SARS-CoV-2 infection were investigated using a human well-differentiated nasal epithelial cell (NEC) model of infection. NECs were observed to release SARS-CoV-2 virus onto the apical surface for up to 28 days post-infection (dpi), further corroborated by viral antigen staining. Single-cell transcriptome sequencing (sc-seq) was utilized to explore the host response from infected NECs after short-term (3-dpi) and long-term (28-dpi) infection. We identified a unique population of cells harboring high viral loads present at both 3 and 28 dpi, characterized by expression of cell stress-related genes DDIT3 and ATF3 and enriched for genes involved in tumor necrosis factor alpha (TNF-alpha) signaling and apoptosis. Remarkably, this sc-seq analysis revealed an antiviral gene signature within all NEC cell types even at 28 dpi. We demonstrate increased replication of basal cells, absence of widespread cell death within the epithelial monolayer, and the ability of SARS-CoV-2 to replicate despite a continuous interferon response as factors likely contributing to SARS-CoV-2 persistence. This study provides a model system for development of therapeutics aimed at improving viral clearance in immunocompromised patients and implies a crucial role for immune cells in mediating viral clearance from infected epithelia. IMPORTANCE Increasing medical attention has been drawn to the persistence of symptoms (long-COVID syndrome) or live virus shedding from subsets of COVID-19 patients weeks to months after the initial onset of symptoms. In vitro approaches to model viral or symptom persistence are needed to fully dissect the complex and likely varied mechanisms underlying these clinical observations. We show that in vitro differentiated human NECs are persistently infected with SARS-CoV-2 for up to 28 dpi. This viral replication occurred despite the presence of an antiviral gene signature across all NEC cell types even at 28 dpi. This indicates that epithelial cell intrinsic antiviral responses are insufficient for the clearance of SARS-CoV-Z implying an essential role for tissue-resident and infiltrating immune cells for eventual viral clearance from infected airway tissue in COVID-19 patients.

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan. IEEE

Digital business transformation in innovation and entrepreneurship

This editorial is written at an unprecedented time in human history, when the entire world is engulfed with the effects of COVID-19 pandemic. The pandemic has taken lives of millions of people, destroyed families, and disrupted the livelihoods of hundreds of millions more. Isolations, lockdowns, and restricted movements threaten to hamper business and unravel the social fabric of the contemporary world. With widespread movement restrictions, human resilience is put to the test, manifesting through the digitalization of businesses, governments, and societies. Consequently, digital business transformation can be conceived as the single most important force to thrive in an exceptional time. In this special issue, we include seven insightful and well-executed research articles that advances contemporary knowledge on digital business transformation in the domains of innovation and entrepreneurship. We believe that these articles are only pertinent to the current circumstances where innovation and entrepreneurship are inevitably digitally-driven, but they are also likely to be relevant beyond the pandemic where digitalization would become the new norm in business transformation. © 2022

Policy recommendations in response to the resurgence of covid-19 cases for institutional medical practice from an ophthalmic perspective

Objectives: The COVID-19 pandemic has been declared a public health emergency of international concern. Singapore was one of the first countries to identify imported cases and also experience a second wave of outbreaks. A slew of measures enacted by the government to ‘flatten the curve’ has directly impacted upon the way we practice. Study design/Methods: This article describes steps enacted by our department to ensure sustainability of our ophthalmic practice. Results: We share considerations at various time points and policies implemented in a stepwise approach in response to the worsening community situation. We further discuss our phased approach towards reinstating our services safely and effectively for patients and staff in a markedly different practice climate. Conclusions: The COVID-19 pandemic has markedly upended the way we practice medicine. Reflecting on the ideal measures required for such occurrences in the future will empower practices with the ability to respond effectively to future outbreaks. © Asian Journal of Ophthalmology.