Tracking the Initial Diffusion of SARS-CoV-2 Omicron Variant in Italy by RT-PCR and Comparison with Alpha and Delta Variants Spreading

Factors Associated With Surveillance Testing in Individuals With COVID-19 Symptoms During the Last Leg of the Pandemic: Multivariable Regression Analysis

Background

Rural underserved areas facing health disparities have unequal access to health resources. By the third and fourth waves of SARS-CoV-2 infections in the United States, COVID-19 testing had reduced, with more reliance on home testing, and those seeking testing were mostly symptomatic.

Objective

This study identifies factors associated with COVID-19 testing among individuals who were symptomatic versus asymptomatic seen at a Rapid Acceleration of Diagnostics for Underserved Populations phase 2 (RADx-UP2) testing site in West Virginia.

Methods

Demographic, clinical, and behavioral factors were collected via survey from tested individuals. Logistic regression was used to identify factors associated with the presence of individuals who were symptomatic seen at testing sites. Global tests for spatial autocorrelation were conducted to examine clustering in the proportion of symptomatic to total individuals tested by zip code. Bivariate maps were created to display geographic distributions between higher proportions of tested individuals who were symptomatic and social determinants of health.

Results

Among predictors, the presence of a physical (adjusted odds ratio [aOR] 1.85, 95% CI 1.3-2.65) or mental (aOR 1.53, 95% CI 0.96-2.48) comorbid condition, challenges related to a place to stay/live (aOR 307.13, 95% CI 1.46-10,6372), no community socioeconomic distress (aOR 0.99, 95% CI 0.98-1.00), no challenges in getting needed medicine (aOR 0.01, 95% CI 0.00-0.82) or transportation (aOR 0.23, 95% CI 0.05-0.64), an interaction between community socioeconomic distress and not getting needed medicine (aOR 1.06, 95% CI 1.00-1.13), and having no community socioeconomic distress while not facing challenges related to a place to stay/live (aOR 0.93, 95% CI 0.87-0.99) were statistically associated with an individual being symptomatic at the first test visit.

Conclusions

This study addresses critical limitations to the current COVID-19 testing literature, which almost exclusively uses population-level disease screening data to inform public health responses.

Multiplexed RT-qPCR Coupled with Whole-Genome Sequencing to Monitor a SARS-CoV-2 Omicron Variant of Concern in a Hospital Laboratory Setting in Latvia

At the end of 2021, the SARS-CoV-2 Omicron variant of concern (VOC) displaced the previously dominant Delta VOC and enhanced diagnostic and therapeutic challenges worldwide. Respiratory specimens submitted to the Riga East University Hospital Laboratory Service by the central and regional hospitals of Latvia from January to March 2022 that were positive for SARS-CoV-2 RNA were tested by commercial multiplexed RT-qPCR targeting three of the Omicron VOC signature mutations: ΔH69/V70, E484A, and N501Y. Of the specimens tested and analyzed in parallel by whole-genome sequencing (WGS), 964 passed the internal quality criteria (genome coverage ≥90%, read depth ≥400×) and the Nextstrain's quality threshold for "good". We validated the detection accuracy of RT-qPCR for each target individually by using WGS as a control. The results were concordant with both approaches for 938 specimens, with the correct classification rate exceeding 96% for each target (CI 95%); however, the presumptive WHO label was misassigned for 21 specimens. The RT-qPCR genotyping provided an acceptable means to pre-monitor the prevalence of the two presumptive Omicron VOC sublineages, BA.1 and BA.2.

Comparison of a Multiplex Real-Time PCR Technique with Oxford Nanopore Technologies Next-Generation Sequencing for Identification of SARS-CoV-2 Variants of Concern

INTRODUCTION

The rapid emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and their potential to endangering the global health has increased the demand for a fast-tracking method in comparison to the next-generation sequencing (NGS) as a gold standard assay, particularly in developing countries. This study was designed to evaluate the performance of a commercial multiplex real-time PCR technique (GA SARS-CoV-2 OneStep RT-PCR Kit, Iran) for identification of SARS-CoV-2 variants of concern (VOCs) compared to the Oxford Nanopore NGS assay.

METHODS

A total of 238 SARS-CoV-2-positive respiratory samples from different waves of COVID-19 in Iran were randomly selected in this study. To determine the SARS-CoV-2 VOC, the samples were analyzed via the commercial triple target assay, GA SARS-CoV-2 OneStep RT-PCR Kit, and NGS as well.

RESULTS

The results revealed good concordance between GA SARS-CoV-2 OneStep RT-PCR Kit and NGS for identification of SARS-CoV-2 VOCs. GA SARS-CoV-2 OneStep RT-PCR Kit identified Wuhan, Alpha, and Delta variants with 100% relative sensitivity and specificity. Regarding Omicron subvariants of BA.1, BA.2, and BA.4/5, the relative sensitivity of 100%, 100%, and 81.5% and the relative specificity of 95.3%, 93.5%, and 100% were observed.

CONCLUSION

Overall, GA SARS-CoV-2 OneStep RT-PCR Kit can be used as a rapid and cost-effective alternative to NGS for identification of SARS-CoV-2 VOCs.

Identification of SARS CoV‐2 Omicron BA.1 and a novel Delta lineage by rapid methods and partial spike protein sequences in Sulaymaniyah Province, Iraq

Background

Five variants of concern (VOCs) of severe acute respiratory syndrome coronavirus 2 (SARS CoV‐2) have been globally recorded including Alpha, Beta, Gamma, Delta, and Omicron. The Omicron variant has outcompeted the other variants including the Delta variant. Molecular screenings of VOCs are important for surveillance, treatment, and vaccination programs. This study aimed to identify VOCs by using rapid inexpensive methods and partial sequencing of the virus's spike gene.

Methods

Mutation‐specific rRT PCR probes were used for both D614G and K417N mutations to potentially discriminate between Delta and Omicron variants. These were followed by sequencing of a fragment of spike gene (748 nucleotides), which covers the most notable VOC mutations in the receptor binding domain of SARS CoV‐2.

Results

Rapid methods showed that out of 24 SARS CoV‐2 positive samples, 19 carried the N417 mutation, which is present in the Omicron variant. Furthermore, 3 samples carried K417 wildtype, which is present in the Delta variant. Additionally, 2 samples containing both K417 and N417 suggested mixed infections between the two variants. The D614G mutation was present in all samples. Among the 4 samples sequenced, 3 samples carried 13 mutations, which are present in Omicron BA.1. The fourth sample contained the two common mutations (T478K and L452R) present in Delta, in addition to two more rare mutations (F456L and F490S), which are not commonly seen in Delta. Our data suggested that both Omicron variant BA.1 and a novel Delta variant might have circulated in this region that needs further investigations.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Analysis of Genetic Variants Associated with COVID-19 Outcome Highlights Different Distributions among Populations

The clinical spectrum of SARS-CoV-2 infection ranges from asymptomatic status to mild infections, to severe disease and death. In this context, the identification of specific susceptibility factors is crucial to detect people at the higher risk of severe disease and improve the outcome of COVID-19 treatment. Several studies identified genetic variants conferring higher risk of SARS-CoV-2 infection and COVID-19 severity. The present study explored their genetic distribution among different populations (AFR, EAS, EUR and SAS). As a result, the obtained data support the existence of a genetic basis for the observed variability among populations, in terms of SARS-CoV-2 infection and disease outcomes. The comparison of ORs distribution for genetic risk of infection as well as for disease outcome shows that each population presents its own characteristics. These data suggest that each country could benefit from a population-wide risk assessment, aimed to personalize the national vaccine programs and the preventative measures as well as the allocation of resources and the access to proper therapeutic interventions. Moreover, the host genetics should be further investigated in order to realize personalized medicine protocols tailored to improve the management of patients suffering from COVID-19.

Validation of reduced S-gene target performance and failure for rapid surveillance of SARS-CoV-2 variants

SARS-CoV-2, the virus that causes COVID-19, has many variants capable of rapid transmission causing serious illness. Timely surveillance of new variants is essential for an effective public health response. Ensuring availability and access to diagnostic and molecular testing is key to this type of surveillance. This study utilized reverse transcription polymerase chain reaction (RT-PCR) and whole genome sequencing results from COVID-19-positive patient samples obtained through a collaboration between Aegis Sciences Corporation and Walgreens Pharmacy that has conducted more than 8.5 million COVID-19 tests at ~5,200 locations across the United States and Puerto Rico. Viral evolution of SARS-CoV-2 can lead to mutations in the S-gene that cause reduced or failed S-gene amplification in diagnostic PCR tests. These anomalies, labeled reduced S-gene target performance (rSGTP) and S-gene target failure (SGTF), are characteristic of variants carrying the del69-70 mutation, such as Alpha and Omicron (B.1.1.529, BA.1, and BA.1.1) lineages. This observation has been validated by whole genome sequencing and can provide presumptive lineage data following completion of diagnostic PCR testing in 24-48 hours from collection. Active surveillance of trends in PCR and sequencing results is key to the identification of changes in viral transmission and emerging variants. This study shows that rSGTP and SGTF can be utilized for near real-time tracking and surveillance of SARS-CoV-2 variants, and is superior to the use of SGTF alone due to the significant proportion of Alpha and Omicron (B.1.1.529, BA.1, and BA.1.1) lineages known to carry the del69-70 mutation and observed to have S-gene amplification. Adopting new tools and techniques to both diagnose acute infections and expedite identification of emerging variants is critical to supporting public health.

A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Development of a simple genotyping method based on indel mutations to rapidly screen SARS-CoV-2 circulating variants: Delta, Omicron BA.1 and BA.2

The high need of rapid and flexible tools that facilitate the identification of circulating SARS-CoV-2 Variants of Concern (VOCs) remains crucial for public health system monitoring.

Here, we develop allele-specific (AS)-qPCR assays targeting three recurrent indel mutations, ΔEF156–157, Ins214EPE and ΔLPP24–26, in spike (S) gene to identify the Delta VOC and the Omicron sublineages BA.1 and BA.2, respectively.

After verification of the analytical specificity of each primer set, two duplex qPCR assays with melting curve analysis were performed to screen 129 COVID-19 cases confirmed between December 31, 2021 and February 01, 2022 in Sfax, Tunisia.

The first duplex assay targeting ΔEF156–157 and Ins214EPE mutations successfully detected the Delta VOC in 39 cases and Omicron BA.1 in 83 cases. All the remaining cases (n = 7) were identified as Omicron BA.2, by the second duplex assay targeting Ins214EPE and ΔLPP24–26 mutations. The results of the screening method were in perfect concordance with those of S gene partial sequencing. In conclusion, our findings provide a simple and flexible screening method for more rapid and reliable monitoring of circulating VOCs. We highly recommend its implementation to guide public health policies.