Corrigendum: Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis.

[This corrects the article DOI: 10.3389/fpubh.2022.961030.].

EvoVGM: A Deep Variational Generative Model for Evolutionary Parameter Estimation

Most evolutionary-oriented deep generative models do not explicitly consider the underlying evolutionary dynamics of biological sequences as it is performed within the Bayesian phylogenetic inference framework. In this study, we propose a method for a deep variational Bayesian generative model (EvoVGM) that jointly approximates the true posterior of local evolutionary parameters and generates sequence alignments. Moreover, it is instantiated and tuned for continuous-Time Markov chain substitution models such as JC69, K80 and GTR. We train the model via a low-variance stochastic estimator and a gradient ascent algorithm. Here, we analyze the consistency and effectiveness of EvoVGM on synthetic sequence alignments simulated with several evolutionary scenarios and different sizes. Finally, we highlight the robustness of a fine-Tuned EvoVGM model using a sequence alignment of gene S of coronaviruses. © 2022 Owner/Author.

Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis.

Purpose: We aim to compare the severity of infections between omicron and delta variants in 609,352 SARS-CoV-2 positive cases using local hospitalization, vaccination, and variants data from the Catalan Health Care System (which covers around 7. 8 million people). Methods: We performed a substitution model to establish the increase in transmissibility of omicron using variant screening data from primary care practices (PCP) and hospital admissions. In addition, we used this data from PCP to establish the two periods when delta and omicron were, respectively, dominant (above 95% of cases). After that, we performed a population-based cohort analysis to calculate the rates of hospital and intensive care unit (ICU) admissions for both periods and to estimate reduction in severity. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status. In a second analysis, the differential substitution model in primary care vs. hospitals allowed us to obtain a population-level average change in severity. Results: We have included 48,874 cases during the delta period and 560,658 during the omicron period. During the delta period, on average, 3.8% of the detected cases required hospitalization for COVID-19. This percentage dropped to 0.9% with omicron [RR of 0.46 (95% CI: 0.43 to 0.49)]. For ICU admissions, it dropped from 0.8 to 0.1% [RR 0.25 (95% CI: 0.21 to 0.28)]. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. The differential substitution models showed an average RR between 0.19 and 0.50. Conclusion: Both independent methods consistently show an important decrease in severity for omicron relative to delta. The systematic reduction happens regardless of age. The severity is also reduced for non-vaccinated and vaccinated groups, but it remains always higher in the non-vaccinated population. This suggests an overall reduction in severity, which could be intrinsic to the omicron variant. The fact is that the RR in ICU admission is systematically smaller than in hospitalization points in the same direction.

Getting through a COVID-19 winter: Physical coldness increases the perceived risk of coronavirus disease.

What factors influence how people perceive the risk of getting COVID-19? Extending beyond features of general health conditions, media coverage, and genetic susceptibility to disease, the present research investigates whether the immediacy of experience with temperature, a subtle yet pervasive environmental factor, can affect people's estimation of contagion probability. According to the attribute substitution model, people may rely on the visceral experience of coldness, a far easier quantity to evaluate, to estimate the contagion probability of the new coronavirus disease. Study 1 found that Chinese university students who perceived the indoor temperature to be lower believed that the coronavirus was more infectious. To provide causal evidence for the effect, Study 2 randomly assigned participants to different conditions. The results showed that participants in the cold condition reported a higher likelihood of contracting the coronavirus than participants in the control condition. Overall, these findings are consistent with the attribute substitution model: people tend to recruit simpler and more accessible information (e.g., local temperature) in place of more diagnostic but less tangible information (e.g., scientific data) in assessing the risk of disease transmission. Theoretical contributions and the significance of this research for policy makers are discussed.

Exploring Diversity of COVID­19 Based on Substitution Distance.

BACKGROUND: The number of COVID-19 infections worldwide has reached 10 million. COVID­19 caused by SARS-CoV-2 is more contagious than SARS-CoV-1. There is a dispute about the origin of COVID-19. Study results showed that all SARS-CoV-2 sequences around the world share a common ancestor towards the end of 2019. METHODS: Virus sequences from COVID-19 samples at the early time should be less diversifiable than those from samples at the later time because there might be more mutations when the virus evolutes over time. The diversity of virus nucleotide sequences can be measured by the nucleotide substitution distance. To explore the diversity of SARS-CoV-2, we use different nucleotide substitution models to calculate the distances of SARS-CoV-2 samples from 3 different areas, China, Europe, and the USA. Then, we use these distances to infer the origin of COVID-19. RESULTS: It is known that COVID-19 originated in Wuhan China and then spread to Europe and the USA. By using different substitution models, the distances of SARS-CoV-2 samples from these areas are significantly different. By ANOVA testing, the p-value is less than 2.2e-16. The analyzed results in most substitution models show that China has the lowest diversity, followed by Europe and lastly by the USA. This outcome coincides with the virus transmission time order that SARS-CoV-2 starts in China, then outbreaks in Europe and finally in the USA. CONCLUSION: The magnitude of nucleotide substitution distance of SARS-CoV-2 is closely related to the transmission time order of SARS-CoV-2. This outcome reveals that the nucleotide substitution distance of SARS-CoV-2 may be used to infer the origin of COVID-19.