Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location.

With the rapid spread of COVID-19 worldwide, viral genomic data are available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis toward the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected-the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this article, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using k-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels.

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F1 scores for the clusters and also better clustering quality metrics compared to baselines.

Experiences of Patients With COVID-19 Admitted to the Intensive Care Units: A Qualitative Study.

This study aimed to describe the experiences of patients with COVID-19 admitted to the intensive care units (ICU). The data were analyzed by content analysis on 16 ICU patients with COVID-19. Data were collected by semi-structured interviews. Three categories were identified: (a) captured by a challenging incident with subcategories: perceived sudden and challenging death, fear of carelessness in overcrowding, worry about the family, and frustration with stigmatizing; (b) the flourishing of life with subcategories: spiritual-awakening, resilience in the face of life challenges, promoting health behaviors, and striving for recovery; and (c) honoring the blessings with subcategories: understanding the importance of nurses, realizing the value of family, and realizing the value of altruism. COVID-19 survivors experienced both positive and negative experiences. The results of this study could help health care providers identify the needs of ICU patients with COVID-19, including psychological, social, and spiritual support and design care models.