Characteristics of the First 284 Patients Infected with the SARS-CoV-2 Omicron BA.2 Subvariant at a Single Center in the Apulia Region of Italy, January-March 2022

The relative prevalence of the Omicron variant within SARS-CoV-2 infected cohorts in different countries: A systematic review

The Omicron variant of SARS-CoV-2 was detected in October 2021 and exhibited high transmissibility, immune evasion, and reduced severity when compared to the earlier variants. The lesser vaccine effectiveness against Omicron and its reduced severity created vaccination hesitancy among the public. This review compiled data reporting the relative prevalence of Omicron as compared to the early variants to give an insight into the existing variants, which may shape the decisions regarding the targets of the newly developed vaccines. Complied data revealed more than 90% prevalence within the infected cohorts in some countries. The BA.1 subvariant predominated over the BA.2 during the early stages of the Omicron wave. Moreover, BA.4/BA.5 subvariants were detected in South Africa, USA and Italy between October 2021 and April 2022. It is therefore important to develop vaccines that protect against Omicron as well as the early variants, which are known to cause more severe complications.

Severity and outcomes of Omicron variant of SARS-CoV-2 compared to Delta variant and severity of Omicron sublineages: a systematic review and metanalysis

OBJECTIVES

To compare severity and clinical outcomes from Omicron as compared with the Delta variant and to compare outcomes between Omicron sublineages.

METHODS

We searched the WHO COVID-19 Research database for studies that compared clinical outcomes for patients with Omicron variant and the Delta variant, and separately Omicron sublineages BA.1 and BA.2. A random-effects meta-analysis was used to pool estimates of relative risk (RR) between variants and sublineages. Heterogeneity between studies was assessed using the I2 index. Risk of bias was assessed using the tool developed by the Clinical Advances through Research and Information Translation team.

RESULTS

Our search identified 1494 studies and 42 met the inclusion criteria. Eleven studies were published as preprints. Of the 42 studies, 29 adjusted for vaccination status; 12 had no adjustment; and for 1, the adjustment was unclear. Three of the included studies compared the sublineages of Omicron BA.1 versus BA.2. As compared with Delta, individuals infected with Omicron had 61% lower risk of death (RR 0.39, 95% CI 0.33 to 0.46) and 56% lower risk of hospitalisation (RR 0.44, 95% CI 0.34 to 0.56). Omicron was similarly associated with lower risk of intensive care unit (ICU) admission, oxygen therapy, and non-invasive and invasive ventilation. The pooled risk ratio for the outcome of hospitalisation when comparing sublineages BA.1 versus BA.2 was 0.55 (95% 0.23 to 1.30).

DISCUSSION

Omicron variant was associated with lower risk of hospitalisation, ICU admission, oxygen therapy, ventilation and death as compared with Delta. There was no difference in the risk of hospitalisation between Omicron sublineages BA.1 and BA.2.

PROSPERO REGISTRATION NUMBER

CRD42022310880.

Application of ultrasensitive assay for SARS-CoV-2 antigen in nasopharynx in the management of COVID-19 patients with comorbidities during the peak of 2022 Shanghai epidemics in a tertiary hospital

OBJECTIVES

Various comorbidities associated with COVID-19 add up in severity of the disease and obviously prolonged the time for viral clearance. This study investigated a novel ultrasensitive MAGLUMI^® SARS-CoV-2 Ag chemiluminescent immunoassay assay (MAG-CLIA) for diagnosis and monitoring the infectivity of COVID-19 patients with comorbid conditions during the pandemic of 2022 Shanghai.

METHODS

Analytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation, linearity and specificity. Nasopharyngeal specimens from 232 hospitalized patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 comorbid participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35.

RESULTS

COVID-19 patients were well-identified using an optimal threshold of 0.64 ng/L antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2-97.9%) and 98.3% (95% CI: 96.7-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8-41.4%) and 100% (95% CI: 99.23-100%), respectively. The sensitivity of MAG-CLIA remained 91.46% (95% CI: 83.14-95.8%) for the samples with Ct values between 35 and 40. Close dynamic consistence was observed between MAG-CLIA and viral load time series in the longitudinal studies. The critical value of 8.82 ng/L antigen showed adequate sensitivity and specificity in evaluating the infectivity of hospitalized convalescent patients with comorbidities.

CONCLUSIONS

The MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of hospitalized convalescent patients with comorbidities.

Projecting the COVID-19 immune landscape in Japan in the presence of waning immunity and booster vaccination

Coronavirus disease 2019 (COVID-19) booster vaccination has been implemented globally in the midst of surges in infection due to the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of the present study was to present a framework to estimate the proportion of the population that is immune to symptomatic SARS-CoV-2 infection with the Omicron variant (immune proportion) in Japan, considering the waning of immunity resulting from vaccination and naturally acquired infection. We quantified the decay rate of immunity against symptomatic infection with Omicron conferred by the second and third doses of COVID-19 vaccine. We estimated the current and future vaccination coverage for the second and third vaccine doses from February 17, 2021 to August 1, 2022 and used data on the confirmed COVID-19 incidence from February 17, 2021 to April 10, 2022. From this information, we estimated the age-specific immune proportion over the period from February 17, 2021 to August 1, 2022. Vaccine-induced immunity, conferred by the second vaccine dose in particular, was estimated to rapidly wane. There were substantial variations in the estimated immune proportion by age group because each age cohort experienced different vaccination rollout timing and speed as well as a different infection risk. Such variations collectively contributed to heterogeneous immune landscape trajectories over time and age. The resulting prediction of the proportion of the population that is immune to symptomatic SARS-CoV-2 infection could aid decision-making on when and for whom another round of booster vaccination should be considered. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

The emergence of SARS-CoV-2 Omicron subvariants: current situation and future trends

The SARS-CoV-2 Omicron variant (B.1.1.529) has been the most recent variant of concern (VOC) established by the World Health Organization (WHO). Because of its greater infectivity and immune evasion, this variant quickly became the dominant type of circulating SARS-CoV-2 worldwide. Our literature review thoroughly explains the current state of Omicron emergence, particularly by comparing different omicron subvariants, including BA.2, BA.1, and BA.3. Such elaboration would be based on structural variations, mutations, clinical manifestation, transmissibility, pathogenicity, and vaccination effectiveness. The most notable difference between the three subvariants is the insufficiency of deletion (Δ69-70) in the spike protein, which results in a lower detection rate of the spike (S) gene target known as (S) gene target failure (SGTF). Furthermore, BA.2 had a stronger affinity to the human Angiotensin-converting Enzyme (hACE2) receptor than other Omicron sub-lineages. Regarding the number of mutations, BA.1.1 has the most (40), followed by BA.1, BA.3, and BA.3 with 39, 34, and 31 mutations, respectively. In addition, BA.2 and BA.3 have greater transmissibility than other sub-lineages (BA.1 and BA.1.1). These characteristics are primarily responsible for Omicron's vast geographical spread and high contagiousness rates, particularly BA.2 sub-lineages.

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.