Vaccination has been instrumental in curbing the transmission of SARS-CoV-2 and mitigating the severity of clinical manifestations associated with COVID-19. Numerous COVID-19 vaccines have been developed to this effect, including BioNTech-Pfizer and Moderna's mRNA vaccines, as well as adenovirus vector-based vaccines such as Oxford-AstraZeneca. However, the emergence of new variants and subvariants of SARS-CoV-2, characterized by enhanced transmissibility and immune evasion, poses significant challenges to the efficacy of current vaccination strategies. In this review, we aim to comprehensively outline the landscape of emerging SARS-CoV-2 variants of concern (VOCs) and sub-lineages that have recently surfaced in the post-pandemic years. We assess the effectiveness of existing vaccines, including their booster doses, against these emerging variants and subvariants, such as BA.2-derived sub-lineages, XBB sub-lineages, and BA.2.86 (Pirola). Furthermore, we discuss the latest advancements in vaccine technology, including multivalent and pan-coronavirus approaches, along with the development of several next-generation coronavirus vaccines, such as exosome-based, virus-like particle (VLP), mucosal, and nanomaterial-based vaccines. Finally, we highlight the key challenges and critical areas for future research to address the evolving threat of SARS-CoV-2 subvariants and to develop strategies for combating the emergence of new viral threats, thereby improving preparedness for future pandemics.

The purpose of the study is to compare the prevalence and associated risk factors of smell and/or taste disorders depending on different virus strains in Hiroshima, Japan.

A cross-sectional design was used.

Data were collected for all COVID-19-confirmed inpatients admitted to 27 hospitals in Hiroshima prefecture, Japan, between 8 April 2020 and 31 January 2023.

Smell and/or taste disorders were indicated by physicians on Hiroshima prefecture COVID-19 version J-SPEED forms completed at discharge.

The COVID-19 data from this period corresponds to the following four strains: Wild-dominant, Alpha-dominant, Delta-dominant and Omicron-dominant. A total of 11 353 confirmed cases were analysed and 1261 cases (11.11%) were reported for smell and/or taste disorders.Among patients with Wild-dominant, 241 out of 1141 cases (21.12%) exhibited smell and/or taste disorders. For Alpha, 223 out of 1265 cases (17.63%), for Delta, 480 out of 1516 cases (31.66%) and for Omicron, 317 out of 7431 cases (4.27%) presented with smell and/or taste disorders. For all four variants, age<65 (Wild: adjusted odds ratio [aOR]=2.66, 95% confidence interval [CI]:1.82-3.88; Alpha:aOR=2.00, 95%CI:1.39-2.88; Delta: aOR=2.42, 95%CI:1.54-3.81; Omicron: aOR=1.84, 95%CI:1.40-2.42) were related to smell and/or taste disorders. For the Wild and Delta variants, higher odds of reporting smell and/or taste disorders were found among wmen (Wild:aOR=1.63, 95%CI:1.20-2.22; Delta: aOR=1.41, 95%CI:1.10- 1.80).

The proportion of patients with smell and/or taste disorders varied significantly depending on the virus strain. Our findings indicate that the Delta-dominant period had the highest number of patients with these disorders, while the Omicron-dominant period had the lowest. Moreover, our study identified risk factors for smell and/or taste disorders for each variant.

The COVID-19 pandemic has caused significant morbidity and mortality worldwide. The emergence of the Alpha and Delta variants of SARS-CoV-2 has led to a renewed interest in using interferon therapy as a potential treatment option. Interferons are a group of signaling proteins produced by host cells in response to viral infections. They play a critical role in the innate immune response to viral infections by inducing an antiviral state in infected and neighboring cells. Interferon therapy has shown promise as a potential treatment option for COVID-19. In this review paper, we review the current knowledge regarding interferon therapy in the context of the Alpha and Delta variants of SARS-CoV-2 and discuss the challenges that must be overcome to translate laboratory findings into effective clinical treatments.

Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.1.7) across 7 months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are subclusters. Mobile service data can indicate these clusters' spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.

COVID-19, caused by SARS-CoV-2, has spread globally, presenting a significant public health challenge. Vaccination has played a critical role in reducing severe disease and deaths. However, the waning of immunity after vaccination and the emergence of immune-escape variants require the continuation of vaccination efforts, including booster doses, to maintain population immunity. This study models the dynamics of COVID-19 in the Basque Country, Spain, aiming to characterize the population's immunity profile and assess its impact on the severity of outbreaks from 2020 to 2022.

A SIR/DS model was developed to analyze the interplay of virus-specific and vaccine-induced immunity. The model includes three levels of immunity, with boosting effects from reinfection and/or vaccination. It was validated using empirical daily case data from the Basque Country. The model tracks shifts in immunity status and their effects on disease dynamics over time.

The COVID-19 epidemic in the Basque Country progressed through three distinct phases, each shaped by dynamic interactions between virus transmission, public health interventions, and vaccination efforts. The initial phase was marked by a rapid surge in cases, followed by a decline due to strict public health measures, with a seroprevalence of 1.3 % . In the intermediate phase, multiple smaller outbreaks emerged as restrictions were relaxed and new variants, such as Alpha and Delta, appeared. During this period, reinfection rates reached 20 % , and seroprevalence increased to 32 % . The final phase, dominated by the Omicron variant, saw a significant rise in cases driven by waning immunity and the variant's high transmissibility. Notably, 34 % of infections during this phase occurred in the naive population, with seroprevalence peaking at 43 % . Across all phases, the infection of naive and unvaccinated individuals contributed significantly to the severity of outbreaks, emphasizing the critical role of vaccination in mitigating disease impact.

The findings underscore the importance of continuous monitoring and adaptive public health strategies to mitigate the evolving epidemiological and immunological landscape of COVID-19. Dynamic interactions between immunity levels, reinfections, and vaccinations are critical in shaping outbreak severity and guiding evidence-based interventions.

Highly mutable pathogens generate viral diversity that impacts virulence, transmissibility, treatment, and thwarts acquired immunity. We previously described C19-SPAR-Seq, a high-throughput, next-generation sequencing platform to detect SARS-CoV-2 that we here deployed to systematically profile variant dynamics of SARS-CoV-2 for over 3 years in a large, North American urban environment (Toronto, Canada). Sequencing of the ACE2 receptor binding motif and polybasic furin cleavage site of the Spike gene in over 70,000 patients revealed that population sweeps of canonical variants of concern (VOCs) occurred in repeating wavelets. Furthermore, we found that VOC mutant derivatives and putative quasispecies that targeted functionally important residues and were found in future VOCs arose frequently, but were always extinguished. Systematic screening of functionally relevant domains in pathogens could thus provide a powerful tool for monitoring spread and mutational trajectories, particularly those with zoonotic potential.

Brazil, Russia, India, China, and South Africa (BRICS) are a group of developing countries with shared economic, healthcare, and scientific interests. These countries navigate multiple syndemics, and the COVID-19 pandemic placed severe strain on already burdened BRICS' healthcare systems, hampering effective pandemic interventions. Genomic surveillance and molecular epidemiology remain indispensable tools for facilitating informed pandemic intervention. To evaluate the combined manner in which the pandemic unfolded in BRICS countries, we reviewed the BRICS pandemic epidemiological and genomic milestones, which included the first reported cases and deaths, and pharmaceutical and non-pharmaceutical interventions implemented in these countries. To assess the development of genomic surveillance capacity and efficiency over the pandemic, we analyzed the turnaround time from sample collection to data availability and the technologies used for genomic analysis. This data provided information on the laboratory capacities that enable the detection of emerging SARS-CoV-2 variants and highlight their potential for monitoring other pathogens in ongoing public health efforts. Our analyses indicated that BRICS suffered >105.6M COVID-19 infections, resulting in >1.7M deaths. BRICS countries detected intricate genetic combinations of SARS-CoV-2 variants that fueled country-specific pandemic waves. BRICS' genomic surveillance programs enabled the identification and characterization of the majority of globally circulating Variants of Concern (VOCs) and their descending lineages. Pandemic intervention strategies first implemented by BRICS countries included non-pharmaceutical interventions during the onset of the pandemic, such as nationwide lockdowns, quarantine procedures, the establishment of fever clinics, and mask mandates- which were emulated internationally. Vaccination rollout strategies complemented this, some representing the first of their kind. Improvements in BRICS sequencing and data generation turnaround time facilitated quicker detection of circulating and emerging variants, supported by investments in sequencing and bioinformatic infrastructure. Intra-BRICS cooperation contributed to the ongoing intervention in COVID-19 and other pandemics, enhancing collective capabilities in addressing these health challenges. The data generated continues to inform BRICS-centric pandemic intervention strategies and influences global health matters. The increased laboratory and bioinformatic capacity post-COVID-19 will support the detection of emerging pathogens.

The emergence of SARS-CoV-2 variants of concern (VOCs) during the COVID-19 pandemic necessitates investigation into their clinical differentiation and outcomes. This study aimed to examine these differences among VOCs, considering multiple related factors. An observational cohort study was conducted on patients diagnosed with SARS-CoV-2 infection via nasopharyngeal/oropharyngeal swab who visited the emergency department of a public Greek hospital between October 2020 and July 2022 during different VOC circulation in the region. Data on clinical manifestations, outcomes, and medical history (comorbidities, prior SARS-CoV-2 infection, vaccination status against COVID-19) were collected through a questionnaire and medical records for those hospitalized. A total of 913 patients were included in this study (813 adults ≥18 years old, 100 children <18 years old). Significant differences were observed across VOCs for both adults and children. A lower proportion of children developed symptoms during the non-Omicron variants, 73.5%, compared to Omicron variants, 86.4%. Fever, dyspnea, and taste and smell disorders were observed more frequently among non-Omicron adult cases, in contrast to upper respiratory symptoms, which were more common symptoms among Omicron infections. The non-Omicron variants were associated with higher rates of hospitalization at 30.6%, pneumonia at 23.0%, and death at 6.1% compared to Omicron variants at 8.0%, 5.0%, and 1.8%, respectively. Vaccination against COVID-19 was shown to be a protective factor for severe outcomes. Our findings suggest distinct clinical presentations and outcomes associated with different VOCs. Despite the fact that current VOCs circulating are less severe, the COVID-19 vaccine continues to play a protective role for severe cases.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) belongs to the genus Beta coronavirus and the family of Coronaviridae. It is a positive-sense, non-segmented single-strand RNA virus. Four common types of human coronaviruses circulate globally, particularly in the fall and winter seasons. They are responsible for 10%-30% of all mild upper respiratory tract infections in adults. These are 229E, NL63 of the Alfacoronaviridae family, OC43, and HKU1 of the Betacoronaviridae family. However, there are three highly pathogenic human coronaviruses: SARS-CoV-2, Middle East respiratory syndrome coronavirus, and the latest pandemic caused by the SARS-CoV-2 infection. All viruses, including SARS-CoV-2, have the inherent tendency to evolve. SARS-CoV-2 is still evolving in humans. Additionally, due to the development of herd immunity, prior infection, use of medication, vaccination, and antibodies, the viruses are facing immune pressure. During the replication process and due to immune pressure, the virus may undergo mutations. Several SARS-CoV-2 variants, including the variants of concern (VOCs), such as B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617/B.1.617.2 (Delta), P.1 (Gamma), and B.1.1.529 (Omicron) have been reported from various parts of the world. These VOCs contain several important mutations; some of them are on the spike proteins. These mutations may lead to enhanced infectivity, transmissibility, and decreased neutralization efficacy by monoclonal antibodies, convalescent sera, or vaccines. Mutations may also lead to a failure of detection by molecular diagnostic tests, leading to a delayed diagnosis, increased community spread, and delayed treatment. We searched PubMed, EMBASE, Covariant, the Stanford variant Database, and the CINAHL from December 2019 to February 2023 using the following search terms: VOC, SARS-CoV-2, Omicron, mutations in SARS-CoV-2, etc. This review discusses the various mutations and their impact on infectivity, transmissibility, and neutralization efficacy.

Socio-economic disparities were associated with disproportionate viral incidence between neighborhoods of New York City (NYC) during the first wave of SARS-CoV-2. We investigated how these disparities affected the co-circulation of SARS-CoV-2 variants during the second wave in NYC. We tested for correlation between the prevalence, in late 2020/early 2021, of Alpha, Iota, Iota with E484K mutation (Iota-E484K), and B.1-like genomes and pre-existing immunity (seropositivity) in NYC neighborhoods. In the context of varying seroprevalence we described socio-economic profiles of neighborhoods and performed migration and lineage persistence analyses using a Bayesian phylogeographical framework. Seropositivity was greater in areas with high poverty and a larger proportion of Black and Hispanic or Latino residents. Seropositivity was positively correlated with the proportion of Iota-E484K and Iota genomes, and negatively correlated with the proportion of Alpha and B.1-like genomes. The proportion of persisting Alpha lineages declined over time in locations with high seroprevalence, whereas the proportion of persisting Iota-E484K lineages remained the same in high seroprevalence areas. During the second wave, the geographic variation of standing immunity, due to disproportionate disease burden during the first wave of SARS-CoV-2 in NYC, allowed for the immune evasive Iota-E484K variant, but not the more transmissible Alpha variant, to circulate in locations with high pre-existing immunity.

This research evaluated the relationship between daily new Coronavirus Disease 2019 (COVID-19) cases and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations in wastewater, followed by effects of differential SARS-CoV-2 shedding loads across various COVID-19 outbreaks. Linear regression analyses were utilized to examine the lead time of the SARS-CoV-2 signal in wastewater relative to new COVID-19 clinical cases. During the Delta wave, no lead time was evident, highlighting limited predictive capability of wastewater monitoring during this phase. However, significant lead times were observed during the Omicron wave, potentially attributed to testing capacity overload and subsequent case reporting delays or changes in shedding patterns. During the Post-Omicron wave (Febuary 23 to May 19, 2022), no lead time was discernible, whereas following the lifting of the COVID-19 state of emergency (May 30, 2022 to May 30, 2023), the correlation coefficient increased and demonstrated the potential of wastewater surveillance as an early warning system. Subsequently, we explored the virus shedding in wastewater through feces, operationalized as the ratio of SARS-CoV-2 concentrations to daily new COVID-19 cases. This ratio varied significantly across the Delta, Omicron, other variants and post-state-emergency phases, with the Kruskal-Wallis H test confirming a significant difference in medians across these stages (P < 0.0001). Despite its promise, wastewater surveillance of COVID-19 disease prevalence presents several challenges, including virus shedding variability, data interpretation complexity, the impact of environmental factors on viral degradation, and the lack of standardized testing procedures. Overall, our findings offer insights into the correlation between COVID-19 cases and wastewater viral concentrations, potential variation in SARS-CoV-2 shedding in wastewater across different pandemic phases, and underscore the promise and limitations of wastewater surveillance as an early warning system for disease prevalence trends.

During the SARS-CoV-2 pandemic, the Dr. Risch medical group employed the multiplex TaqPathTM COVID-19 CE-IVD RT-PCR Kit for large-scale routine diagnostic testing in Switzerland and the principality of Liechtenstein. The TaqPath Kit is a widely used multiplex assay targeting three genes (i.e., ORF1AB, N, S). With emergence of the B.1.1.7 (Alpha) variant, a diagnostic flaw became apparent as the amplification of the S-gene target was absent in these samples due to a deletion (ΔH69/V70) in the Alpha variant genome. This S-gene target failure (SGTF) was the earliest indication of a new variant emerging and was also observed in subsequent variants such as Omicron BA.1 and BA4/BA.5. The Delta variant and Omicron BA.2 did not present with SGTF. From September 2020 to November 2022, we investigated the applicability of the SGTF as a surrogate marker for emerging variants such as B.1.1.7, B.1.617.2 (Delta), and Omicron BA.1, BA.2, and BA.4/BA.5 in samples with cycle threshold (Ct) values < 30. Next to true SGTF-positive and SGTF-negative samples, there were also samples presenting with delayed-type S-gene amplification (higher Ct value for S-gene than ORF1ab gene). Among these, a difference of 3.8 Ct values between the S- and ORF1ab genes was found to best distinguish between "true" SGTF and the cycle threshold variability of the assay. Samples above the cutoff were subsequently termed partial SGTF (pSGTF). Variant confirmation was performed by whole-genome sequencing (Oxford Nanopore Technology, Oxford, UK) or mutation-specific PCR (TIB MOLBIOL). In total, 17,724 (7.4%) samples among 240,896 positives were variant-confirmed, resulting in an overall sensitivity and specificity of 93.2% [92.7%, 93.7%] and 99.3% [99.2%, 99.5%], respectively. Sensitivity was increased to 98.2% [97.9% to 98.4%] and specificity lowered to 98.9% [98.6% to 99.1%] when samples with pSGTF were included. Furthermore, weekly logistic growth rates (α) and sigmoid's midpoint (t0) were calculated based on SGTF data and did not significantly differ from calculations based on comprehensive data from GISAID. The SGTF therefore allowed for a valid real-time estimate for the introduction of all dominant variants in Switzerland and Liechtenstein.

As the COVID-19 pandemic has evolved during the past years, interactions between human immune systems, rapidly mutating and selected SARS-CoV-2 viral variants, and effective vaccines have complicated the landscape of individual immunological histories. Here, we review some key findings for antibody and B cell-mediated immunity, including responses to the highly mutated omicron variants; immunological imprinting and other impacts of successive viral antigenic variant exposures on antibody and B cell memory; responses in secondary lymphoid and mucosal tissues and non-neutralizing antibody-mediated immunity; responses in populations vulnerable to severe disease such as those with cancer, immunodeficiencies, and other comorbidities, as well as populations showing apparent resistance to severe disease such as many African populations; and evidence of antibody involvement in postacute sequelae of infection or long COVID. Despite the initial phase of the pandemic ending, human populations will continue to face challenges presented by this unpredictable virus.

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.

The rapid development of mRNA vaccines against SARS-CoV-2 has revolutionized vaccinology, offering hope for swift responses to emerging infectious diseases. Initially met with skepticism, mRNA vaccines have proven effective and safe, reducing vaccine hesitancy amid the evolving COVID-19 pandemic. The COVID-19 pandemic has demonstrated that the time required to modify mRNA vaccines to counter new mutant strains is significantly shorter than the time it takes for pathogens to mutate and generate new variants that can thrive in vaccinated populations. This highlights the notion that mRNA vaccine technology appears to be outpacing viruses in the ongoing evolutionary race.

This review article offers valuable insights into several crucial aspects of mRNA vaccine development and deployment, including the fundamentals of mRNA vaccine design and synthesis, the utilization of delivery systems, considerations regarding vaccine safety, the longevity of the immune response, strategies for modifying the original mRNA vaccine to address emerging mutant strains, as well as addressing vaccine hesitancy and potential approaches to mitigate reluctance.

Challenges such as stability, storage, manufacturing complexities, production capacity, allergic reactions, long-term effects, accessibility, and misinformation must be addressed. Despite these hurdles, mRNA vaccine technology holds promise for revolutionizing future vaccination strategies.

To evaluate the ability of a neural network to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using point-of-care electrocardiography obtained with a portable device.

We enrolled 2827 patients in a prospective observational study, from December 10, 2020, through June 4, 2021, to determine the accuracy of a point-of-care, handheld, smartphone-compatible, artificial intelligence-enabled electrocardiography (ECG) (POC AI-ECG) in detecting asymptomatic SARS-CoV-2 infection using a modified version of an existing deep learning model framework trained on 12-lead ECG data.

Study participants were 48% (n=1067) female, 79% (n=1749) White, and 7% (n=153) endorsed previous COVID-19 infection. We found the POC AI-ECG algorithm was ineffective for detecting asymptomatic SARS-CoV-2 infection (area under curve, 0.56; 95% CI, 0.46-0.66), failing to adequately discriminate between ECGs performed among participants who tested positive compared to those who tested negative.

Contrary to the prior 12-lead ECG study, a POC AI-ECG failed to reliably identify asymptomatic SARS-CoV-2 infection among adults. This study underscores the importance of prospective testing, assuring similar populations, and using similar signals or data when developing AI-ECG tools.

clinicaltrials.gov Identifier: NCT04725097.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

At the end of 2021, we observed an increase in N-gene target failures (NGTF) with the TaqPathTM COVID-19 CE-IVD RT-PCR Kit from Thermo Fisher Scientific (TaqPath). We subsequently used whole-genome sequencing (Oxford Nanopore Technology) to identify potential issues with N-gene PCR efficacy. Among 168,101 positive samples with a cycle threshold (CT) value <30 from August 2021 to May 2022, 194 specimens without N-gene amplification by PCR were identified (0.12%). Most NGTF samples originated from a wave of infection attributable to the Delta variant (B.1.617.2) and its sublineages. Sequencing revealed the nucleotide substitution G28922T (A217S) in 151 samples (88.8%). The substitution G215C, a hallmark mutation for Delta lineages, was concurrently present in all of these samples. Ten samples (5.9%) carried the deletion 28,913-28,918 (del214/215), eight samples (4.7%) the deletion 28,913-28,915 (del214) and one sample (0.6%) the deletion 28,892-28,930 (del207-219). Samples showing intact N-gene amplification by PCR lacked these specific mutations, but delayed-type amplification (i.e., partial or pNGTF) was attributable to the exclusive presence of A217S. As the N gene is a common target in many RT-PCR methods for SARS-CoV-2, an in-depth analysis of single-target failures using a combination with viral whole genome sequencing may allow for the identification of diagnostic flaws and eventual new variants.

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.

The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.

A multitude of demographic, health, and genetic factors are associated with the risk of developing severe COVID-19 following infection by the SARS-CoV-2. There is a need to perform studies across human societies and to investigate the full spectrum of genetic variation of the virus. Using data from 869 COVID-19 patients in Bahrain between March 2020 and March 2021, we analyzed paired viral sequencing and non-genetic host data to understand host and viral determinants of severe COVID-19. We estimated the effects of demographic variables specific to the Bahrain population and found that the impact of health factors are largely consistent with other populations. To extend beyond the common variants of concern in the Spike protein analyzed by previous studies, we used a viral burden approach and detected a protective effect of low-frequency missense viral mutations in the RNA-dependent RNA polymerase (Pol) gene on disease severity. Our results contribute to the survey of severe COVID-19 in diverse populations and highlight the benefits of studying rare viral mutations.

Early detection of increased infections or new variants of SARS-CoV-2 is critical for public health response. To determine whether cycle threshold (Ct) data from PCR tests for SARS-CoV-2 could serve as an early indicator of epidemic growth, we analyzed daily mean Ct values in England, UK, by gene target and used iterative sequential regression to detect break points in mean Ct values (and positive test counts). To monitor the epidemic in England, we continued those analyses in real time. During September 2020-January 2022, a total of 7,611,153 positive SARS-CoV-2 PCR test results with Ct data were reported. Spike (S) gene target (S+/S-)-specific mean Ct values decreased 6-29 days before positive test counts increased, and S-gene Ct values provided early indication of increasing new variants (Delta and Omicron). Our approach was beneficial in the context of the first waves of the COVID-19 pandemic and can be used to support future infectious disease monitoring.

BackgroundDuring the COVID-19 pandemic, the Danish National Institute for Infectious Disease, Statens Serum Institute (SSI) developed a home-based SARS-CoV-2 surveillance system.AimsWe wanted to determine whether a cohort of individuals performing self-administered swabs for PCR at home could support surveillance of SARS-CoV-2, including detection and assessment of new variants. We also aimed to evaluate the logistical setup.MethodsFrom May to July 2022, 10,000 blood donors were invited to participate, along with their household members. Participation required performing a self-swab for 4 consecutive weeks and answering symptom questionnaires via a web app. Swabs were sent by post to SSI for PCR analysis and whole genome sequencing. After study completion, participants were asked to complete a questionnaire concerning their experience.ResultsIn total, 2,186 individuals enrolled (47.4% blood donors), and 1,333 performed self-swabbing (53.0 blood donors), of whom 48 had at least one SARS-CoV-2-positive sample. Fourteen different Omicron subvariants, primarily BA.5 subvariants, were identified by whole genome sequencing (WGS). In total, 29 of the 63 SARS-CoV-2-positive samples were taken from individuals who were asymptomatic at the time of swabbing. Participants collected 2.9 swabs on average, with varying intervals between swabs. Transmission within households was observed in only three of 25 households.ConclusionParticipants successfully performed self-swabs and answered symptom questionnaires. Also, WGS analysis of samples was possible. The system can support surveillance of respiratory pathogens and also holds potential as a diagnostic tool, easing access to test for at-risk groups, while also reducing the burden on healthcare system resources.

During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.

Although representing the majority of newly diagnosed cancers, patients with breast cancer appear less vulnerable to COVID-19 mortality compared with other malignancies. In the absence of patients on active cancer therapy included in vaccination trials, a contemporary real-world evaluation of outcomes during the various pandemic phases, as well as of the impact of vaccination, is needed to better inform clinical practice.

We compared COVID-19 morbidity and mortality among patients with breast cancer across prevaccination (February 27, 2020-November 30, 2020), Alpha-Delta (December 1, 2020-December 14, 2021), and Omicron (December 15, 2021-January 31, 2022) phases using OnCovid registry participants (ClinicalTrials.gov identifier: NCT04393974). Twenty-eight-day case fatality rate (CFR28) and COVID-19 severity were compared in unvaccinated versus double-dosed/boosted patients (vaccinated) with inverse probability of treatment weighting models adjusted for country of origin, age, number of comorbidities, tumor stage, and receipt of systemic anticancer therapy within 1 month of COVID-19 diagnosis.

By the data lock of February 4, 2022, the registry counted 613 eligible patients with breast cancer: 60.1% (n = 312) hormone receptor-positive, 25.2% (n = 131) human epidermal growth factor receptor 2-positive, and 14.6% (n = 76) triple-negative. The majority (61%; n = 374) had localized/locally advanced disease. Median age was 62 years (interquartile range, 51-74 years). A total of 193 patients (31.5%) presented ≥ 2 comorbidities and 69% (n = 330) were never smokers. In total, 392 (63.9%), 164 (26.8%), and 57 (9.3%) were diagnosed during the prevaccination, Alpha-Delta, and Omicron phases, respectively. Analysis of CFR28 demonstrates comparable estimates of mortality across the three pandemic phases (13.9%, 12.2%, 5.3%, respectively; P = .182). Nevertheless, a significant improvement in outcome measures of COVID-19 severity across the three pandemic time periods was observed. Importantly, when reported separately, unvaccinated patients from the Alpha-Delta and Omicron phases achieved comparable outcomes to those from the prevaccination phase. Of 566 patients eligible for the vaccination analysis, 72 (12.7%) were fully vaccinated and 494 (87.3%) were unvaccinated. We confirmed with inverse probability of treatment weighting multivariable analysis and following a clustered robust correction for participating center that vaccinated patients achieved improved CFR28 (odds ratio [OR], 0.19; 95% CI, 0.09 to 0.40), hospitalization (OR, 0.28; 95% CI, 0.11 to 0.69), COVID-19 complications (OR, 0.16; 95% CI, 0.06 to 0.45), and reduced requirement of COVID-19-specific therapy (OR, 0.24; 95% CI, 0.09 to 0.63) and oxygen therapy (OR, 0.24; 95% CI, 0.09 to 0.67) compared with unvaccinated controls.

Our findings highlight a consistent reduction of COVID-19 severity in patients with breast cancer during the Omicron outbreak in Europe. We also demonstrate that even in this population, a complete severe acute respiratory syndrome coronavirus 2 vaccination course is a strong determinant of improved morbidity and mortality from COVID-19.

COVID-19 sequelae can affect about 15% of patients with cancer who survive the acute phase of SARS-CoV-2 infection and can substantially impair their survival and continuity of oncological care. We aimed to investigate whether previous immunisation affects long-term sequelae in the context of evolving variants of concern of SARS-CoV-2.

OnCovid is an active registry that includes patients aged 18 years or older from 37 institutions across Belgium, France, Germany, Italy, Spain, and the UK with a laboratory-confirmed diagnosis of COVID-19 and a history of solid or haematological malignancy, either active or in remission, followed up from COVID-19 diagnosis until death. We evaluated the prevalence of COVID-19 sequelae in patients who survived COVID-19 and underwent a formal clinical reassessment, categorising infection according to the date of diagnosis as the omicron (B.1.1.529) phase from Dec 15, 2021, to Jan 31, 2022; the alpha (B.1.1.7)-delta (B.1.617.2) phase from Dec 1, 2020, to Dec 14, 2021; and the pre-vaccination phase from Feb 27 to Nov 30, 2020. The prevalence of overall COVID-19 sequelae was compared according to SARS-CoV-2 immunisation status and in relation to post-COVID-19 survival and resumption of systemic anticancer therapy. This study is registered with ClinicalTrials.gov, NCT04393974.

At the follow-up update on June 20, 2022, 1909 eligible patients, evaluated after a median of 39 days (IQR 24-68) from COVID-19 diagnosis, were included (964 [50·7%] of 1902 patients with sex data were female and 938 [49·3%] were male). Overall, 317 (16·6%; 95% CI 14·8-18·5) of 1909 patients had at least one sequela from COVID-19 at the first oncological reassessment. The prevalence of COVID-19 sequelae was highest in the pre-vaccination phase (191 [19·1%; 95% CI 16·4-22·0] of 1000 patients). The prevalence was similar in the alpha-delta phase (110 [16·8%; 13·8-20·3] of 653 patients, p=0·24), but significantly lower in the omicron phase (16 [6·2%; 3·5-10·2] of 256 patients, p<0·0001). In the alpha-delta phase, 84 (18·3%; 95% CI 14·6-22·7) of 458 unvaccinated patients and three (9·4%; 1·9-27·3) of 32 unvaccinated patients in the omicron phase had sequelae. Patients who received a booster and those who received two vaccine doses had a significantly lower prevalence of overall COVID-19 sequelae than unvaccinated or partially vaccinated patients (ten [7·4%; 95% CI 3·5-13·5] of 136 boosted patients, 18 [9·8%; 5·8-15·5] of 183 patients who had two vaccine doses vs 277 [18·5%; 16·5-20·9] of 1489 unvaccinated patients, p=0·0001), respiratory sequelae (six [4·4%; 1·6-9·6], 11 [6·0%; 3·0-10·7] vs 148 [9·9%; 8·4-11·6], p=0·030), and prolonged fatigue (three [2·2%; 0·1-6·4], ten [5·4%; 2·6-10·0] vs 115 [7·7%; 6·3-9·3], p=0·037).

Unvaccinated patients with cancer remain highly vulnerable to COVID-19 sequelae irrespective of viral strain. This study confirms the role of previous SARS-CoV-2 immunisation as an effective measure to protect patients from COVID-19 sequelae, disruption of therapy, and ensuing mortality.

UK National Institute for Health and Care Research Imperial Biomedical Research Centre and the Cancer Treatment and Research Trust.

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.