Genomic Epidemiology of SARS-CoV-2 Infection During the Initial Pandemic Wave and Association With Disease Severity

Intra-host mutation rate of acute SARS-CoV-2 infection during the initial pandemic wave

SARS-CoV-2 mutation is minimized through a proofreading function encoded by NSP-14. Most estimates of the SARS-CoV-2 mutation rate are derived from population based sequence data. Our understanding of SARS-CoV-2 evolution might be enhanced through analysis of intra-host viral mutation rates in specific populations. Viral genome analysis was performed between paired samples and mutations quantified at allele frequencies (AF) ≥ 0.25, ≥ 0.5 and ≥ 0.75. Mutation rate was determined employing F81 and JC69 evolution models and compared between isolates with (ΔNSP-14) and without (wtNSP-14) non-synonymous mutations in NSP-14 and by patient comorbidity. Forty paired samples with median interval of 13 days [IQR 8.5-20] were analyzed. The estimated mutation rate by F81 modeling was 93.6 (95%CI 90.8-96.4], 40.7 (95%CI 38.9-42.6) and 34.7 (95%CI 33.0-36.4) substitutions/genome/year at AF ≥ 0.25, ≥ 0.5, ≥ 0.75 respectively. Mutation rate in ΔNSP-14 were significantly elevated at AF ≥ 0.25 vs wtNSP-14. Patients with immune comorbidities had higher mutation rate at all allele frequencies. Intra-host SARS-CoV-2 mutation rates are substantially higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated mutation rate at low AF. Immunosuppressed patients have elevated mutation rate at all AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.

[COVID 19 - Hospital Admission in the First and Second Wave in Germany]

PURPOSE

 We analyzed patients' characteristics and hospital admission in Germany's first and second COVID 19 wave.

METHODS

 We include all patients hospitalized with the proven diagnosis COVID 19 admitted to the HELIOS Hospital Krefeld, Germany, in the first wave (n = 84; from 11.03.2020-30.06.2020) and the second wave (n = 344; from 01.07.2020-31.01.2021).

RESULTS

 Patients' age, gender and comorbidities were similar with the exception of venous thrombosis in medical history which was more frequent in the first wave (6 % vs 0.3 %, p = p = 0,001). At admission, there were no differences in the results of the initial lab values (c-reactive protein, leucocytes) and blood gas analyses between both groups. Treatment differed in the application of dexamethasone and anticoagulation. In the first wave, nobody received dexamethasone. However, this changed to 52.6 % of patients in the second wave for a mean length of 3.6 ± 4.1 days. Anticoagulation with double standard prophylaxis (2 × 40 mg low molecular heparin, subcutaneous) was applied in 7.1 % of patients in the first wave but 30.2 % (p = 0.002) in the second wave. In the first wave more thromboembolic events were diagnosed after admission (19.0 % vs 7.0 %, p = 0.001). In-hospital death was 26.2 % in the first wave and 15.4 % in the second wave (p = 0.0234). Most deaths were attributed to acute respiratory distress syndrome (ARDS).

CONCLUSION

 Patients' characteristics did not vary in Germany's first and second COVID 19 wave, but anticoagulation and dexamethasone were applied more frequently in the second wave. In addition, there were fewer thromboembolic complications in the second wave.

Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era

Global SARS-CoV-2 genomic surveillance efforts have provided critical data on the ongoing evolution of the virus to inform best practices in clinical care and public health throughout the pandemic. Impactful genomic surveillance strategies generally follow a multi-disciplinary pipeline involving clinical sample collection, viral genotyping, metadata linkage, data reporting, and public health responses. Unfortunately, current limitations in each of these steps have compromised the overall effectiveness of these strategies. Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants. The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Limitations and inconsistencies in clinical and demographic data collection and sharing can slow the compilation and limit the utility of comprehensive datasets. This likewise can complicate data reporting, restricting the availability of timely data. Finally, gaps and delays in the implementation of genomic surveillance data in the public health sphere can prevent officials from formulating effective mitigation strategies to prevent outbreaks. In this review, we outline current SARS-CoV-2 global genomic surveillance methods and assess roadblocks at each step of the pipeline to identify potential solutions. Evaluating the current obstacles that impede effective surveillance can improve both global coordination efforts and pandemic preparedness for future outbreaks.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

Background

Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

Method

Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

Results

In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P < .001; χ2 = 21.6, P < .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

Conclusions

Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

BACKGROUND

Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

METHOD

Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

RESULTS

In total 2779 patients identified with either Alpha (n 1153), Gamma (n 122), Delta (n 808), or Omicron variants (n 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (2 12.8, P .001; 2 21.6, P .002; 2 9.6, P .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9] and 513/696 [73.7], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

CONCLUSIONS

Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Biophysical and Biochemical Characterization of the Receptor Binding Domain of SARS-CoV-2 Variants

The newly emerging SARS-CoV-2 variants are potential threat and posing new challenges for medical intervention due to high transmissibility and escaping neutralizing antibody (NAb) responses. Many of these variants have mutations in the receptor binding domain (RBD) of SARS-CoV-2 spike protein that interacts with the host cell receptor. Rapid mutation in the RBD through natural selection to improve affinity for host receptor and antibody pressure from vaccinated or infected individual will greatly impact the presently adopted strategies for developing interventions. Understanding the nature of mutations and how they impact the biophysical, biochemical and immunological properties of the RBD will help immensely to improve the intervention strategies. To understand the impact of mutation on the protease sensitivity, thermal stability, affinity for the receptor and immune response, we prepared several mutants of soluble RBD that belong to the variants of concern (VoCs) and interest (VoIs) and characterize them. Our results show that the mutations do not impact the overall structure of the RBD. However, the mutants showed increase in the thermal melting point, few mutants were more sensitive to protease degradation, most of them have enhanced affinity for ACE2 and some of them induced better immune response compared to the parental RBD.

Host genetic loci LZTFL1 and CCL2 associated with SARS-CoV-2 infection and severity of COVID-19

OBJECTIVES

Host genetic factors contribute to the variable severity of COVID-19. We examined genetic variants from genome-wide association studies and candidate gene association studies in a cohort of patients with COVID-19 and investigated the role of early SARS-CoV-2 strains in COVID-19 severity.

METHODS

This case-control study included 123 COVID-19 cases (hospitalized or ambulatory) and healthy controls from the state of Baden-Wuerttemberg, Germany. We genotyped 30 single nucleotide polymorphisms, using a custom-designed panel. Cases were also compared with the 1000 genomes project. Polygenic risk scores were constructed. SARS-CoV-2 genomes from 26 patients with COVID-19 were sequenced and compared between ambulatory and hospitalized cases, and phylogeny was reconstructed.

RESULTS

Eight variants reached nominal significance and two were significantly associated with at least one of the phenotypes "susceptibility to infection", "hospitalization", or "severity": rs73064425 in LZTFL1 (hospitalization and severity, P <0.001) and rs1024611 near CCL2 (susceptibility, including 1000 genomes project, P = 0.001). The polygenic risk score could predict hospitalization. Most (23/26, 89%) of the SARS-CoV-2 genomes were classified as B.1 lineage. No associations of SARS-CoV-2 mutations or lineages with severity were observed.

CONCLUSION

These host genetic markers provide insights into pathogenesis and enable risk classification. Variants which reached nominal significance should be included in larger studies.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

Genomic Epidemiology of SARS-CoV-2 in Tocantins State and the Diffusion of P.1.7 and AY.99.2 Lineages in Brazil

Tocantins is a state in the cross-section between the Central-West, North and Northeast regions of Brazilian territory; it is a gathering point for travelers and transportation from the whole country. In this study, 9493 genome sequences, including 241 local SARS-CoV-2 samples (collected from 21 December 2020, to 16 December 2021, and sequenced in the MinION platform) were analyzed with the following aims: (i) identify the relative prevalence of SARS-CoV-2 lineages in the state of Tocantins; (ii) analyze them phylogenetically against global SARS-CoV-2 sequences; and (iii) hypothesize the viral dispersal routes of the two most abundant lineages found in our study using phylogenetic and phylogeographic approaches. The performed analysis demonstrated that the majority of the strains sequenced during the period belong to the Gamma P.1.7 (32.4%) lineage, followed by Delta AY.99.2 (27.8%), with the first detection of VOC Omicron. As expected, there was mainly a dispersion of P.1.7 from the state of São Paulo to Tocantins, with evidence of secondary spreads from Tocantins to Goiás, Mato Grosso, Amapá, and Pará. Rio de Janeiro was found to be the source of AY.99.2 and from then, multiple cluster transmission was observed across Brazilian states, especially São Paulo, Paraiba, Federal District, and Tocantins. These data show the importance of trade routes as pathways for the transportation of the virus from Southeast to Northern Brazil.

An issue of concern: unique truncated ORF8 protein variants of SARS-CoV-2

Open reading frame 8 (ORF8) shows one of the highest levels of variability among accessory proteins in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID-19). It was previously reported that the ORF8 protein inhibits the presentation of viral antigens by the major histocompatibility complex class I (MHC-I), which interacts with host factors involved in pulmonary inflammation. The ORF8 protein assists SARS-CoV-2 in evading immunity and plays a role in SARS-CoV-2 replication. Among many contributing mutations, Q27STOP, a mutation in the ORF8 protein, defines the B.1.1.7 lineage of SARS-CoV-2, engendering the second wave of COVID-19. In the present study, 47 unique truncated ORF8 proteins (T-ORF8) with the Q27STOP mutations were identified among 49,055 available B.1.1.7 SARS-CoV-2 sequences. The results show that only one of the 47 T-ORF8 variants spread to over 57 geo-locations in North America, and other continents, which include Africa, Asia, Europe and South America. Based on various quantitative features, such as amino acid homology, polar/non-polar sequence homology, Shannon entropy conservation, and other physicochemical properties of all specific 47 T-ORF8 protein variants, nine possible T-ORF8 unique variants were defined. The question as to whether T-ORF8 variants function similarly to the wild type ORF8 is yet to be investigated. A positive response to the question could exacerbate future COVID-19 waves, necessitating severe containment measures.

SARS-CoV-2 Mutations and COVID-19 Clinical Outcome: Mutation Global Frequency Dynamics and Structural Modulation Hold the Key

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an enormous burden on the healthcare system worldwide as a consequence of its new emerging variants of concern (VOCs) since late 2019. Elucidating viral genome characteristics and its influence on disease severity and clinical outcome has been one of the crucial aspects toward pandemic management. Genomic surveillance holds the key to identify the spectrum of mutations vis-à-vis disease outcome. Here, in our study, we performed a comprehensive analysis of the mutation distribution among the coronavirus disease 2019 (COVID-19) recovered and mortality patients. In addition to the clinical data analysis, the significant mutations within the two groups were analyzed for their global presence in an effort to understand the temporal dynamics of the mutations globally in comparison with our cohort. Interestingly, we found that all the mutations within the recovered patients showed significantly low global presence, indicating the possibility of regional pool of mutations and the absence of preferential selection by the virus during the course of the pandemic. In addition, we found the mutation S194L to have the most significant occurrence in the mortality group, suggesting its role toward a severe disease progression. Also, we discovered three mutations within the mortality patients with a high cohort and global distribution, which later became a part of variants of interest (VOIs)/VOCs, suggesting its significant role in enhancing viral characteristics. To understand the possible mechanism, we performed molecular dynamics (MD) simulations of nucleocapsid mutations, S194L and S194*, from the mortality and recovered patients, respectively, to examine its impacts on protein structure and stability. Importantly, we observed the mutation S194* within the recovered to be comparatively unstable, hence showing a low global frequency, as we observed. Thus, our study provides integrative insights about the clinical features, mutations significantly associated with the two different clinical outcomes, its global presence, and its possible effects at the structural level to understand the role of mutations in driving the COVID-19 pandemic.

SARS-CoV-2 Variants and Clinical Outcomes: A Systematic Review

Background: From the start of the COVID-19 pandemic, new SARS-CoV-2 variants have emerged that potentially affect transmissibility, severity, and immune evasion in infected individuals. In the present systematic review, the impact of different SARS-CoV-2 variants on clinical outcomes is analyzed. Methods: A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Two databases (PubMed and ScienceDirect) were searched for original articles published from 1 January 2020 to 23 November 2021. The articles that met the selection criteria were appraised according to the Newcastle–Ottawa Quality Assessment Scale. Results: Thirty-three articles were included, involving a total of 253,209 patients and 188,944 partial or complete SARS-CoV-2 sequences. The most reported SARS-CoV-2 variants showed changes in the spike protein, N protein, RdRp and NSP3. In 28 scenarios, SARS-CoV-2 variants were found to be associated with a mild to severe or even fatal clinical outcome, 15 articles reported such association to be statistically significant. Adjustments in eight of them were made for age, sex and other covariates. Conclusions: SARS-CoV-2 variants can potentially have an impact on clinical outcomes; future studies focused on this topic should consider several covariates that influence the clinical course of the disease.

An issue of concern: unique truncated ORF8 protein variants of SARS-CoV-2

Open reading frame 8 (ORF8) shows one of the highest levels of variability among accessory proteins in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID-19). It was previously reported that the ORF8 protein inhibits the presentation of viral antigens by the major histocompatibility complex class I (MHC-I), which interacts with host factors involved in pulmonary inflammation. The ORF8 protein assists SARS-CoV-2 in evading immunity and plays a role in SARS-CoV-2 replication. Among many contributing mutations, Q27STOP, a mutation in the ORF8 protein, defines the B.1.1.7 lineage of SARS-CoV-2, engendering the second wave of COVID-19. In the present study, 47 unique truncated ORF8 proteins (T-ORF8) with the Q27STOP mutations were identified among 49,055 available B.1.1.7 SARS-CoV-2 sequences. The results show that only one of the 47 T-ORF8 variants spread to over 57 geo-locations in North America, and other continents, which include Africa, Asia, Europe and South America. Based on various quantitative features, such as amino acid homology, polar/non-polar sequence homology, Shannon entropy conservation, and other physicochemical properties of all specific 47 T-ORF8 protein variants, nine possible T-ORF8 unique variants were defined. The question as to whether T-ORF8 variants function similarly to the wild type ORF8 is yet to be investigated. A positive response to the question could exacerbate future COVID-19 waves, necessitating severe containment measures.

Whole-Genome Sequencing of Pathogenic Bacteria-New Insights into Antibiotic Resistance Spreading

In recent years, the acquisition of antimicrobial resistance (AMR) by both pathogenic and opportunistic bacteria has become a major problem worldwide, which was already noticed as a global healthcare threat by the World Health Organization [...].

Jeopardy of COVID-19: Rechecking the Perks of Phytotherapeutic Interventions

The novel coronavirus disease (COVID-19), the reason for worldwide pandemic, has already masked around 220 countries globally. This disease is induced by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). Arising environmental stress, increase in the oxidative stress level, weak immunity and lack of nutrition deteriorates the clinical status of the infected patients. Though several researches are at its peak for understanding and bringing forward effective therapeutics, yet there is no promising solution treating this disease directly. Medicinal plants and their active metabolites have always been promising in treating many clinical complications since time immemorial. Mother nature provides vivid chemical structures, which act multi-dimensionally all alone or synergistically in mitigating several diseases. Their unique antioxidant and anti-inflammatory activity with least side effects have made them more effective candidate for pharmacological studies. These medicinal plants inhibit attachment, encapsulation and replication of COVID-19 viruses by targeting various signaling molecules such as angiotensin converting enzyme-2, transmembrane serine protease 2, spike glycoprotein, main protease etc. This property is re-examined and its potency is now used to improve the existing global health crisis. This review is an attempt to focus various antiviral activities of various noteworthy medicinal plants. Moreover, its implications as prophylactic or preventive in various secondary complications including neurological, cardiovascular, acute kidney disease, liver disease are also pinpointed in the present review. This knowledge will help emphasis on the therapeutic developments for this novel coronavirus where it can be used as alone or in combination with the repositioned drugs to combat COVID-19.

Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

The outcome of SARS-CoV-2 infection is determined by multiple factors, including the viral, host genetics, age, and comorbidities. This study investigated the association between prognostic factors and disease outcomes of patients infected by SARS-CoV-2 with multiple S protein mutations. Fifty-one COVID-19 patients were recruited in this study. Whole-genome sequencing of 170 full-genomes of SARS-CoV-2 was conducted with the Illumina MiSeq sequencer. Most patients (47%) had mild symptoms of COVID-19 followed by moderate (19.6%), no symptoms (13.7%), severe (4%), and critical (2%). Mortality was found in 13.7% of the COVID-19 patients. There was a significant difference between the age of hospitalized patients (53.4 ± 18 years) and the age of non-hospitalized patients (34.6 ± 19) (p = 0.001). The patients' hospitalization was strongly associated with hypertension, diabetes, and anticoagulant and were strongly significant with the OR of 17 (95% CI 2-144; p = 0.001), 4.47 (95% CI 1.07-18.58; p = 0.039), and 27.97 (95% CI 1.54-507.13; p = 0.02), respectively; while the patients' mortality was significantly correlated with patients' age, anticoagulant, steroid, and diabetes, with OR of 8.44 (95% CI 1.5-47.49; p = 0.016), 46.8 (95% CI 4.63-472.77; p = 0.001), 15.75 (95% CI 2-123.86; p = 0.009), and 8.5 (95% CI 1.43-50.66; p = 0.019), respectively. This study found the clade: L (2%), GH (84.3%), GR (11.7%), and O (2%). Besides the D614G mutation, we found L5F (18.8%), V213A (18.8%), and S689R (8.3%). No significant association between multiple S protein mutations and the patients' hospitalization or mortality. Multivariate analysis revealed that hypertension and anticoagulant were the significant factors influencing the hospitalization and mortality of patients with COVID-19 with an OR of 17.06 (95% CI 2.02-144.36; p = 0.009) and 46.8 (95% CI 4.63-472.77; p = 0.001), respectively. Moreover, the multiple S protein mutations almost reached a strong association with patients' hospitalization (p = 0.07). We concluded that hypertension and anticoagulant therapy have a significant impact on COVID-19 outcomes. This study also suggests that multiple S protein mutations may impact the COVID-19 outcomes. This further emphasized the significance of monitoring SARS-CoV-2 variants through genomic surveillance, particularly those that may impact the COVID-19 outcomes.

Covid-19 Infection in India: A Comparative Analysis of the Second Wave with the First Wave

Coronavirus disease 2019 (COVID-19) is considered as the most dreaded disease that has spread all over the world in the recent past. Despite its outbreak in December 2019–January 2020, a few continents and countries such as India started to experience a significant number of COVID-19-positive cases from March 2020. GISAID clade variation analysis in the period March 2020–February 2021 (period I) and March 2021–first week of April 2021 (period II) showed a rapid variation of SARS-CoV-2 in all continents and India over time. Studying the relationship of patient age or gender with viral clades in these two periods revealed that the population under 10 years of age was the least affected, whereas the 11–60-year-old population was the most affected, irrespective of patient gender and ethnicity. In the first wave, India registered quite a low number of COVID-19-positive cases/million people, but the scenario unexpectedly changed in the second wave, when even over 400,000 confirmed cases/day were reported. Lineage analysis in India showed the emergence of new SARS-CoV-2 variants, i.e., B.1.617.1 and B.1.617.2, during April–May 2021, which might be one of the key reasons for the sudden upsurge of confirmed cases/day. Furthermore, the emergence of the new variants contributed to the shift in infection spread by the G clade of SARS-CoV-2 from 46% in period II to 82.34% by the end of May 2021. Along with the management of the emergence of new variants, few factors viz., lockdown and vaccination were also accountable for controlling the upsurge of new COVID-19 cases throughout the country. Collectively, a comparative analysis of the scenario of the first wave with that of the second wave would suggest policymakers the way to prepare for better management of COVID-19 recurrence or its severity in India and other countries.

Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.

Relative risks of COVID-19 fatality between the first and second waves of the pandemic in Ontario, Canada

Objectives

To examine whether the case fatality rate (CFR) of COVID-19 decreased over time and whether the COVID-19 testing rate is a driving factor for the changes if the CFR decreased.

Methods

Analyzing COVID-19 cases, deaths and tests in Ontario, Canada, we compared the CFR between the first wave and the second wave across 26 public health units in Ontario. We also explored whether a high testing rate was associated with a large CFR decrease.

Results

The first wave CFR ranged from 0.004 to 0.146, whereas the second wave CFR ranged from 0.003 to 0.034. The pooled RR estimate of second wave COVID-19 case fatality, compared with first wave, was 0.24 (95% CI: 0.19-0.32). Additionally, COVID-19 testing percentages were not associated with the estimated relative risk (P=0.246).

Conclusions

The COVID-19 CFR decreased significantly in Ontario during the second wave, and COVID-19 testing was not a driving factor for this decrease.

SARS-CoV-2 Infectivity and Severity of COVID-19 According to SARS-CoV-2 Variants: Current Evidence

BACKGROUND

We conducted this review to summarize the relation between viral mutation and infectivity of SARS-CoV-2 and also the severity of COVID-19 in vivo and in vitro.

METHOD

Articles were identified through a literature search until 31 May 2021, in PubMed, Web of Science and Google Scholar.

RESULTS

Sixty-three studies were included. To date, most studies showed that the viral mutations, especially the D614G variant, correlate with a higher infectivity than the wild-type virus. However, the evidence of the association between viral mutation and severity of the disease is scant. A SARS-CoV-2 variant with a 382-nucleotide deletion was associated with less severe infection in patients. The 11,083G > U mutation was significantly associated with asymptomatic patients. By contrast, ORF1ab 4715L and S protein 614G variants were significantly more frequent in patients from countries where high fatality rates were also reported. The current evidence showed that variants of concern have led to increased infectivity and deteriorating epidemiological situations. However, the relation between this variant and severity of COVID-19 infection was contradictory.

CONCLUSION

The COVID-19 pandemic continues to spread worldwide. It is necessary to anticipate large clinical cohorts to evaluate the virulence and transmissibility of SARS-CoV-2 mutants.

Patient Characteristics and Clinical Course of COVID-19 Patients Treated at a German Tertiary Center during the First and Second Waves in the Year 2020

In this study, we directly compared coronavirus disease 2019 (COVID-19) patients hospitalized during the first (27 February-28 July 2020) and second (29 July-31 December 2020) wave of the pandemic at a large tertiary center in northern Germany. Patients who presented during the first (n = 174) and second (n = 331) wave did not differ in age (median [IQR], 59 years [46, 71] vs. 58 years [42, 73]; p = 0.82) or age-adjusted Charlson Comorbidity Index (median [IQR], 2 [1, 4] vs. 2 [0, 4]; p = 0.50). During the second wave, a higher proportion of patients were treated as outpatients (11% [n = 20] vs. 20% [n = 67]), fewer patients were admitted to the intensive care unit (43% [n = 75] vs. 29% [n = 96]), and duration of hospitalization was significantly shorter (median days [IQR], 14 [8, 34] vs. 11 [5, 19]; p < 0.001). However, in-hospital mortality was high throughout the pandemic and did not differ between the two periods (16% [n = 27] vs. 16% [n = 54]; p = 0.89). While novel treatment strategies and increased knowledge about the clinical management of COVID-19 may have resulted in a less severe disease course in some patients, in-hospital mortality remained unaltered at a high level. These findings highlight the unabated need for efforts to hamper severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) transmission, to increase vaccination coverage, and to develop novel treatment strategies to prevent mortality and decrease morbidity.