SARS-CoV-2 emerging Omicron subvariants with a special focus on BF.7 and XBB.1.5 recently posing fears of rising cases amid ongoing COVID-19 pandemic

Genomic surveillance of SARS-CoV-2 and emergence of XBB.1.16 variant in Rajasthan

PURPOSE

Genomic surveillance of positive SARS-CoV-2 samples is important to monitor the genetic changes occurring in virus, this was enhanced after the WHO designation of XBB.1.16 as a variant under monitoring in March 2023. From 5th February till May 6, 2023 all positive SARS-CoV-2 samples were monitored for genetic changes.

METHODS

A total of 1757 samples having Ct value <25 (for E and ORF gene) from different districts of Rajasthan were processed for Next Generation Sequencing (NGS). The FASTA files obtained on sequencing were used for lineage determination using Nextclade and phylogenetic tree construction.

RESULTS AND CONCLUSIONS

Sequencing and lineage identification was done in 1624 samples. XBB.1.16 was the predominant lineage in 1413 (87.0%) cases while rest was other XBB (207, 12.74%) and other lineages (4, 0.2%). Of the 1413 XBB.1.16 cases, 57.47% were males and 42.53% were females. Majority (66.53%) belonged to 19-59 year age. 84.15% of XBB.1.16 cases were infected for the first time. Hospitalization was required in only 2.2% cases and death was reported in 5 (0.35%) patients. Most of the cases were symptomatic and the commonest symptoms were fever, cough and rhinorrhea. Co-morbidities were present in 414 (29.3%) cases. Enhanced genomic surveillance helped to rapidly identify the spread of XBB variant in Rajasthan. This in turn helped to take control measures to prevent spread of virus and estimate public health risks of the new variant relative to the previously circulating lineages. XBB variant was found to spread rapidly but produced milder disease.

Systematic detection of co-infection and intra-host recombination in more than 2 million global SARS-CoV-2 samples

Systematic monitoring of SARS-CoV-2 co-infections between different lineages and assessing the risk of intra-host recombinant emergence are crucial for forecasting viral evolution. Here we present a comprehensive analysis of more than 2 million SARS-CoV-2 raw read datasets submitted to the European COVID-19 Data Portal to identify co-infections and intra-host recombination. Co-infection was observed in 0.35% of the investigated cases. Two independent procedures were implemented to detect intra-host recombination. We show that sensitivity is predominantly determined by the density of lineage-defining mutations along the genome, thus we used an expanded list of mutually exclusive defining mutations of specific variant combinations to increase statistical power. We call attention to multiple challenges rendering recombinant detection difficult and provide guidelines for the reduction of false positives arising from chimeric sequences produced during PCR amplification. Additionally, we identify three recombination hotspots of Delta - Omicron BA.1 intra-host recombinants.

A review of SARS-CoV-2 variants and vaccines: Viral properties, mutations, vaccine efficacy, and safety

The severe acute respiratory syndrome coronavirus disease 2 instigated by coronavirus disease of 2019 (COVID-19) has delivered an unfathomable obstruction that has touched all sectors worldwide. Despite new vaccine technologies and mass administration of booster doses, the virus persists, and unknown the ending of the pandemic for new variants and sub-variants. Moreover, whether leaning on home medications or using plant extracts is sufficient often to combat the virus has generated tremendous interest in the scientific fraternity. Different databases including PubMed, Scopus, Web of Science, and Google Scholar used to find published articles linked with related topics. Currently, COVID-19 third and fourth shots of vaccines are progressively administered worldwide, where some countries trail others by a significant margin. Many proteins related to viral activity have changed, possibly boosting the virus infectivity and making antibodies ineffective. This study will reminisce the viral genome, associated pathways for viral protein functions, variants, and their mutations. The current, comprehensive review will also provide information on vaccine technologies developed by several biotech companies and the efficacy of their doses, costs including boosters on a mass level. As no vaccine is working to protect fully against all the variants, the new proactive vaccine research needs to be conducted based on all variants, their sub-lineage, and mutations.

A meta-meta-analysis of co-infection, secondary infections, and antimicrobial resistance in COVID-19 patients

The newly discovered coronavirus SARS-CoV-2 has sparked a worldwide pandemic of COVID-19, which has caused havoc on medical infrastructures, economies, and cultures around the world. Determining the whole scenario is essential since SARS-CoV-2 variants and sub-variants keep appearing after vaccinations and booster doses. The objective of this secondary meta-analysis is to analysis co-infection, secondary infections, and antimicrobial resistance (AMR) in COVID-19 patients. This study used five significant databases to conduct a systematic review and an overlap meta-analysis to evaluate the pooled estimates of co-infections and secondary infections. The summary of the meta-analysis showed an overall co-infection effect of 26.19% (95% confidence intervals CI: 21.39-31.01, I2 =98.78, n = 14 meta-analysis) among patients with COVID-19. A coinfection effect of 11.13% (95% CI: 9.7-12.56, I2 =99.14, n = 11 meta-analysis) for bacteria; 9.69% (95% CI: 1.21-7.90, I2 =98.33) for fungal and 3.48% (95% CI: 2.15-4.81, I2 =95.84) for viruses. A secondary infection effect of 19.03% (95% CI: 9.53-28.54, I2 =85.65) was pooled from 2 meta-analyses (Ave: 82 primary studies). This is the first study that compiles the results of all the previous three years meta-analyses into a single source and offers strong proof of co-infections and secondary infections in COVID-19 patients. Early detection of co-infection and AMR is crucial for COVID-19 patients in order to effective treatment.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning

This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation N=22i×22i,(i=3) yields a greater profit. Performance studies indicate that, for BER = 10-3, there are gains of -10 [dB], -3 [dB], 3 [dB], and 5 [dB] for N=22i×22i,(i=0,1,2,3), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a recall of 99% for positive values.