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Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

Simple Summary: The family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains. The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 related symptoms and severity of infection vary greatly among individuals and althoμgh various factors including age, gender, obesity, and underlying co-morbidities are sμggested to be the confounding variables that affect the severity of disease, a small percentage of apparently healthy and young people also succumb to disease, this was especially true before the COVID-19 vaccines were developed. Recently, few studies have sμggested that COVID-19 severity is lower in individuals who followed a plant based and pescatarian diet. Aim of the current study was to examine whether symptoms experienced by COVID-19 patients can be predictive of mild and long-term disease pre-vaccination and if the intake of specific nutrients as well as serum 25(OH)D concentrations are related to the severity of symptoms experienced by individuals. Additionally, we performed longitudinal and cross-sectional analysis on antibodies against SARS-CoV-2 to examine the duration and degree of protection conferred by natural immunity after SARS-CoV-2 infection. We found several nutrients such as vitamin E, poly-unsaturated fatty acids, and fibre amongst others to be possibly linked to a reduced COVID-19 severity. The heterogeneity of the severity of symptoms of COVID-19 experienced by the young and healthy individuals is poorly understood. The present study was undertaken to mainly examine whether the respective diets and the type of symptoms experienced by patients are predictive of having long COVID-19. Disease severity was assessed with a symptomatology questionnaire and used to group 55 participants in asymptomatic (AS), mild symptoms (S) and long COVID (LC). We found that experiencing a higher number of symptoms as well as fatigue were predictors of developing LC whereas those who experienced rhinorrhea were less likely to develop LC. Blood samples were also taken to measure vitamin D [25(OH)D] concentrations and duration of spike IgG antibodies. In this study, serum 25(OH)D was not significantly different between 3 symptom groups with median (IQR) ng/mL levels of 22.0 (12.3) in the AS, 22.3 (7.5) in S, and 24.9 (9.4) in the LC group (p ≥ 0.05). The duration of IgG antibody response was found to vary greatly, with some individuals showing raised IgG over a year after infection. To examine whether dietary factors can influence the severity of symptoms, diet was analysed using 4–7-day food diaries as well as a Food Frequency Questionnaire (FFQ). Some nutrients such as vitamin E, polyunsaturated fatty acids, fibre, and iron were associated with lower severity of COVID-19. Lower intake of vitamin E was associated with having LC with a median (IQR) intake of 6.2 mg (3.8) seen in LC vs. 8.6 mg (7.2) in the AS group (p = 0.047). This pilot study has highlighted a few differences in the number and type of symptoms experienced by the young non-hospitalised individuals with mild and long COVID-19 and identified a few dietary components for their potential protective role against long COVID-19, however, the findings need to be confirmed with further large scale studies. [ABSTRACT FROM AUTHOR]