Airport-based pathogen monitoring is a critical tool that can contribute to early detection and characterization of existing and new pathogen threats. A novel public-private partnership between an airport spa group, a biotech company, and the Centers for Disease Control and Prevention was instrumental in establishing a multimodal pathogen genomic surveillance program at US international airports. That public-private partnership addressed critical challenges that neither party could overcome independently, resulting in the development and deployment of a scalable, flexible early warning system for pathogen detection and public health monitoring.

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences. In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. We use SARS-CoV-2 spike protein as the target and use a protein language model to predict novel mutations. Different from existing research which is often limited to mutating the reference sequence for prediction, we propose a parent-child mutation prediction paradigm where a parent sequence is modeled for mutation prediction. Because mutations introduce changing context to the underlying sequence, DNMS models three aspects of the protein sequences: semantic changes, grammatical changes, and attention changes, each modeling protein sequence aspects from shifting of semantics, grammar coherence, and amino-acid interactions in latent space. A ranking approach is proposed to combine all three aspects to capture mutations demonstrating evolving traits, in accordance with real-world SARS-CoV-2 spike protein sequence evolution. DNMS can be adopted for an early warning variant detection system, creating public health awareness of future SARS-CoV-2 mutations.

Several variants of SARS-CoV-2 have a demonstrated impact on public health, including high and increased transmissibility, severity of infection, and immune escape. Therefore, this study aimed to determine the SARS-CoV-2 lineages and better characterize the dynamics of the pandemic during the different waves in Guinea.

Whole genome sequencing of 363 samples with PCR cycle threshold (Ct) values under thirty was undertaken between May 2020 and May 2023. The Illumina iSeq 100 technology was used. The sequences were then analyzed using the GeVarli pipeline to generate consensus sequences and variant calling. All sequences isolated in Guinea and available on GISAID were included in the analysis for phylogenetic tree and phylodynamic determination. Nextstain tools were used for these analyses. Statistical analysis was done using GraphPad Prism version 10.

The circulation of SARS-CoV-2 in Guinea can be distributed in three different periods. The first, lasting from May to June 2020, was characterized by lineages B1 and B.1.1. The second period, from January 2021 to July 2021, was characterized by the lineages B.1.1.7 (Alpha), AY.122, B.1.1.318, R1, B.1.525 and B.1.629. The third period, between December 2021 and May 2023, was characterized by the Omicron variant, with nine subvariant majorities found. In addition, detecting variants in the period out of their circulation was documented. The importation and exportation investigation showed the strong movement viral association between Guinea and Senegal on the one hand and Guinea and Nigeria on the other.

In summary, this study contributes to understanding the epidemic dynamics of the disease by describing the significant variants that circulated in Guinee and the distribution of this variant in the population. It also shows the importation and exportation of the virus during the pandemic. Sub-sampling and degradation of samples for sequences were observed. Organization and collaboration between laboratories are needed for a good sample-collecting and storage system for future direction.

Toll-like receptors (TLRs) of human are considered as the most critical immunological mediators of inflammatory pathogenesis of COVID-19. These immunoregulatory glycoproteins are located on the surface and/or intracellular compartment act as innate immune sensors. Upon binding with distinct SARS-CoV-2 ligand(s), TLRs signal activation of different transcription factors that induce expression of the proinflammatory mediators that collectively induce 'cytokine storm'. Similarly, TLR activation is also pivotal in conferring protection to infection and invasion as well as upregulating the tissue repair pathways. This dual role of the human TLRs in deciding the fate of SARS-CoV-2 has made these receptor proteins as the critical mediators of immunoprotective and immunopathogenic consequences associated with COVID-19. Herein, pathbreaking discoveries exploring the immunobiological importance of the TLRs in COVID-19 and developing TLR-directed therapeutic intervention have been reviewed by accessing the up-to-date literatures available in the public domain/databases. In accordance with our knowledge in association with the importance of TLRs' role against viruses and identification of viral particles, they have been recognized as suitable candidates with high potential as vaccine adjuvants. In this regard, the agonists of TLR4 and TLR9 have effective potential in vaccine technology while the others need further investigations. This comprehensive review suggests that basal level expression of TLRs can act as friends to keep our body safe from strangers but act as a foe via overexpression. Therefore, selective inhibition of the overexpressed TLRs appears to be a solution to counteract the cytokine storm while TLR-agonists as vaccine adjuvants could lessen the risk of infection in the naïve population.

Given the challenges that SARS-CoV-2 variants have caused in terms of rapid spread and reduced vaccine efficacy, a rapid and cost-effective assay that can detect new and emerging variants is greatly needed worldwide. We have successfully applied the xenonucleic acid-based molecular-clamping technology to develop a multiplex reverse-transcription quantitative real-time PCR assay for SARS-CoV-2 multivariant detection. The assay was used to test 649 nasopharyngeal swab samples that were collected for clinical diagnosis or surveillance. The assay was able to correctly identify all 36 Delta variant samples as it accurately detected the D614G, T478K, and L452R mutations. In addition, the assay was able to correctly identify all 34 Omicron samples by detecting the K417N, T478K, N501Y, and D614G mutations. This technique reliably detects a variety of variants and has an analytical sensitivity of 100 copies/mL. In conclusion, this novel assay can serve as a rapid and cost-effective tool to facilitate large-scale detection of SARS-CoV-2 variants.

We have developed a multiplex reverse-transcription quantitative real-time PCR (RT-qPCR) testing platform for the rapid detection of SARS-CoV-2 variants using the xenonucleic acid (XNA)-based molecular-clamping technology. The XNA-based RT-qPCR assay can achieve high sensitivity with a limit of detection of about 100 copies/mL for variant detection which is much better than the next-generation sequencing (NGS) assay. Its turnaround time is about 4 hours with lower cost and a lot of Clinical Laboratory Improvement Amendments (CLIA) labs own the instrument and meet skillset requirements. This assay provides a rapid, reliable, and cost-effective testing platform for rapid detection and monitoring of known and emerging SARS-CoV-2 variants. This testing platform can be adopted by laboratories that perform routine SARS-CoV-2 PCR testing, providing a rapid and cost-effective method in lieu of NGS-based assays, for detecting, differentiating, and monitoring SARS-CoV-2 variants. This assay is easily scalable to any new variant(s) should it emerge.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19, and so far, it has occurred five noteworthy variants of concern (VOC). SARS-CoV-2 invades cells by contacting its Spike (S) protein to its receptor on the host cell, angiotensin-converting enzyme 2 (ACE2). However, the high frequency of mutations in the S protein has limited the effectiveness of existing drugs against SARS-CoV-2 variants, particularly the Omicron variant. Therefore, it is critical to develop drugs that have highly effective antiviral activity against both SARS-CoV-2 and its variants in the future. This review provides an overview of the mechanism of SARS-CoV-2 infection and the current progress on anti-SARS-CoV-2 drugs.

The coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represents the most disastrous infectious disease pandemic of the past century. As a member of the Betacoronavirus genus, the SARS-CoV-2 genome encodes a total of 29 proteins. The spike protein, RNA-dependent RNA polymerase, and proteases play crucial roles in the virus replication process and are promising targets for drug development. In recent years, structural studies of these viral proteins and of their complexes with antibodies and inhibitors have provided valuable insights into their functions and laid a solid foundation for drug development. In this review, we summarize the structural features of these proteins and discuss recent progress in research regarding therapeutic development, highlighting mechanistically representative molecules and those that have already been approved or are under clinical investigation.

The implementation genomic-based surveillance on emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in low-income countries, which have inadequate molecular and sequencing capabilities and limited vaccine storage, represents a challenge for public health. To date, there is little evidence on molecular investigations of SARS-CoV-2 variants in areas where they might emerge. We report the findings of an experimental SARS-CoV-2 molecular surveillance programme for migrants, refugees, and asylum seekers arriving to Europe via Italy through the Mediterranean Sea.

We descriptively analysed data on migrants collected at entry points in Sicily from February 2021 to May 2022. These entry points are integrated with a network of laboratories fully equipped for molecular analyses, which performed next-generation sequencing and used Nextclade and the Pangolin coronavirus disease 2019 (COVID-19) tools for clade/lineage assignment.

We obtained 472 full-length SARS-CoV-2 sequences and identified 12 unique clades belonging to 31 different lineages. The delta variant accounted for 43.6% of all genomes, followed by clades 21D (Eta) and 20A (25.4% and 11.4%, respectively). Notably, some of the identified lineages (A.23.1, A.27, and A.29) predicted their introduction into the migration area. The mutation analysis allowed us to identify 617 different amino acid substitutions, 156 amino acid deletions, 7 stop codons, and 6 amino acid insertions. Lastly, we highlighted the geographical distribution patterns of some mutational profiles occurring in the migrants' countries of origin.

Genome-based molecular surveillance dedicated to migrant populations from low-resource areas may be useful for forecasting new epidemiological scenarios related to SARS-CoV-2 variants or other emerging pathogens, as well as for informing the updating of vaccination strategies.

Physical activity (PA) is known to decrease COVID-19 risk factors and can attenuate symptoms of viral infections. However, difficulty exercising and fatigue are common complaints after COVID-19. It is unknown whether prior habitual PA will improve outcomes and what the time course is until full recovery of PA after COVID-19.

Invitations were emailed to 21,933 adults who were SARS-CoV-2 positive between March 2020 and February 2021. Participants completed intake surveys and the Physical Activity History (PAH) questionnaire regarding PA during the 3-month prior to infection. Monthly thereafter, for up to 23 months, participants were emailed surveys. Scores were computed for moderate and heavy PA. Long COVID (LC) was defined as having recurring/persistent symptoms 9 months after diagnosis.

Overall, 993 patients completed the intake survey (age 50.7±15.8 years, BMI 27.3±9.2, 58% women); 28% had been hospitalized. One-third had recovered to their pre-infection level of PA at 9 months post-infection; this increased to 65% at one year, and 90% at two years. Higher pre-diagnosis PA reduced odds of hospitalization (P<0.05) but not of LC. Factors predictive of poor PA recovery were higher pre-diagnosis PA, shortness of breath and fatigue during acute illness, and fatigue chronically. Participants who reported ongoing symptoms had consistently poorer recovery of habitual PA compared to those not reporting chronic symptoms.

Habitual PA reduced odds of hospitalization but not of LC. Thirty-five percent had not returned to pre-COVID-19 levels of PA one year after infection, representing a major public health threat.

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, but the underestimate was considerable only when > 50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI, 93.6-98.6) and 85.5% (95% CI, 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater has been reported in several studies and similar research can be used as a proxy for an early warning of potential Coronavirus disease 2019 (COVID-19) outbreaks. This study focused on profiling the incidence of SARS-CoV-2 genomes in wastewater samples obtained from facilities located in the Buffalo City Municipality. Raw samples were collected weekly using the grab technique for a period of 48 weeks. Ribonucleic acids were extracted from the samples, using the QIAGEN Powersoil Total RNA Extraction kit, and extracted RNA samples were further profiled for the presence of SARS-CoV-2 genomes using Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) technique. Furthermore, various environmental matrices were utilized to estimate the potential health risk to plant operators associated with exposure to SARS-CoV-2 viral particles using the quantitative microbiological risk assessment (QMRA) model. Our findings revealed the prevalence of SARS-CoV-2 genomes with concentrations that ranged from 0.22 × 103 to 17.60 × 103 genome copies per milliliter (GC/mL). Different exposure scenarios were employed for the QMRA model, and the findings indicate a probability of infection (P(i)) ranging from 0.93% to 37.81% across the study sites. Similarly, the P(i) was highly significant (p < 0.001) for the 20 mL volumetric intake as compared to other volumetric intake scenarios, and high P(i) was also observed in spring, autumn, and winter for all WWTPs. The P(i) was significantly different (p < 0.05) with respect to the different seasons and with respect to different volume scenarios.

In the United States, the growing number of people experiencing homelessness has become a socioeconomic crisis with public health ramifications, recently exacerbated by the COVID-19 pandemic. We hypothesized that the environmental surveillance of flood control infrastructure may be an effective approach to understand the prevalence of infectious disease. From December 2021 through July 2022, we tested for SARS-CoV-2 RNA from two flood control channels known to be impacted by unsheltered individuals residing in upstream tunnels. Using qPCR, we detected SARS-CoV-2 RNA in these environmental water samples when significant COVID-19 outbreaks were occurring in the surrounding community. We also performed whole genome sequencing to identify SARS-CoV-2 lineages. Variant compositions were consistent with those of geographically and temporally matched municipal wastewater samples and clinical specimens. However, we also detected 10 of 22 mutations specific to the Alpha variant in the environmental water samples collected during January 2022-one year after the Alpha infection peak. We also identified mutations in the spike gene that have never been identified in published reports. Our findings demonstrate that environmental surveillance of flood control infrastructure may be an effective tool to understand public health conditions among unsheltered individuals-a vulnerable population that is underrepresented in clinical surveillance data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into numerous lineages with unique spike mutations and caused multiple epidemics domestically and globally. Although COVID-19 vaccines are available, new variants with the capacity for immune evasion continue to emerge. To understand and characterize the evolution of circulating SARS-CoV-2 variants in the U.S., the Centers for Disease Control and Prevention (CDC) initiated the National SARS-CoV-2 Strain Surveillance (NS3) program and has received thousands of SARS-CoV-2 clinical specimens from across the nation as part of a genotype to phenotype characterization process. Focus reduction neutralization with various antisera was used to antigenically characterize 143 SARS-CoV-2 Delta, Mu and Omicron subvariants from selected clinical specimens received between May 2021 and February 2023, representing a total of 59 unique spike protein sequences. BA.4/5 subvariants BU.1, BQ.1.1, CR.1.1, CQ.2 and BA.4/5 + D420N + K444T; BA.2.75 subvariants BM.4.1.1, BA.2.75.2, CV.1; and recombinant Omicron variants XBF, XBB.1, XBB.1.5 showed the greatest escape from neutralizing antibodies when analyzed against post third-dose original monovalent vaccinee sera. Post fourth-dose bivalent vaccinee sera provided better protection against those subvariants, but substantial reductions in neutralization titers were still observed, especially among BA.4/5 subvariants with both an N-terminal domain (NTD) deletion and receptor binding domain (RBD) substitutions K444M + N460K and recombinant Omicron variants. This analysis demonstrated a framework for long-term systematic genotype to antigenic characterization of circulating and emerging SARS-CoV-2 variants in the U.S., which is critical to assessing their potential impact on the effectiveness of current vaccines and antigen recommendations for future updates.

Current global COVID-19 booster scheduling strategies mainly focus on vaccinating high-risk populations at predetermined intervals. However, these strategies overlook key data: the direct insights into individual immunity levels from active serological testing and the indirect information available either through sample-based sero-surveillance, or vital demographic, location, and epidemiological factors. Our research, employing an age-, risk-, and region-structured mathematical model of disease transmission-based on COVID-19 incidence and vaccination data from Israel between 15 May 2020 and 25 October 2021-reveals that a more comprehensive strategy integrating these elements can significantly reduce COVID-19 hospitalizations without increasing existing booster coverage. Notably, the effective use of indirect information alone can considerably decrease COVID-19 cases and hospitalizations, without the need for additional vaccine doses. This approach may also be applicable in optimizing vaccination strategies for other infectious diseases, including influenza.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is the cause of coronavirus disease 2019 (COVID-19); a severe respiratory distress that has emerged from the city of Wuhan, Hubei province, China during December 2019. COVID-19 is currently the major global health problem and the disease has now spread to most countries in the world. COVID-19 has profoundly impacted human health and activities worldwide. Genetic mutation is one of the essential characteristics of viruses. They do so to adapt to their host or to move to another one. Viral genetic mutations have a high potentiality to impact human health as these mutations grant viruses unique unpredicted characteristics. The difficulty in predicting viral genetic mutations is a significant obstacle in the field. Evidence indicates that SARS-CoV-2 has a variety of genetic mutations and genomic diversity with obvious clinical consequences and implications. In this review, we comprehensively summarized and discussed the currently available knowledge regarding SARS-CoV-2 outbreaks with a fundamental focus on the role of the viral proteins and their mutations in viral infection and COVID-19 progression. We also summarized the clinical implications of SARS-CoV-2 variants and how they affect the disease severity and hinder vaccine development. Finally, we provided a massive phylogenetic analysis of the spike gene of 214 SARS-CoV-2 isolates from different geographical regions all over the world and their associated clinical implications.

The evolutionary dynamics of viruses, particularly exemplified by SARS-CoV-2 during the ongoing COVID-19 pandemic, underscore the intricate interplay between genetics, host adaptation, and viral spread. This paper delves into the genetic evolution of SARS-CoV-2, emphasizing the implications of viral variants on global health. Initially emerging from the Wuhan-Hu-1 lineage, SARS-CoV-2 rapidly diversified into numerous variants, each characterized by distinct mutations in the spike protein and other genomic regions. Notable variants such as B.1.1.7 (α), B.1.351 (β), P.1 (γ), B.1.617.2 (δ), and the Omicron variant have garnered significant attention due to their heightened transmissibility and immune evasion capabilities. In particular, the Omicron variant has presented a myriad of subvariants, raising concerns about its potential impact on public health. Despite the emergence of numerous variants, the vast majority have exhibited limited expansion capabilities and have not posed significant threats akin to early pandemic strains. Continued genomic surveillance is imperative to identify emerging variants of concern promptly. While genetic adaptation is intrinsic to viral evolution, effective public health responses must be grounded in empirical evidence to navigate the evolving landscape of the pandemic with resilience and precision.

Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities.

COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS.

Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected.

We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

EPOCH-US is an ongoing, retrospective, observational cohort study among individuals identified in the Healthcare Integrated Research Database (HIRD®) with ≥ 12 months of continuous health plan enrollment. Data were collected for the HIRD population (containing immunocompetent and immunocompromised [IC] individuals), individual IC cohorts (non-mutually exclusive cohorts based on immunocompromising condition and/or immunosuppressive [IS] treatment), and the composite IC population (all unique IC individuals). This study updates previous results with addition of the general population cohort and data specifically for the year of 2022 (i.e., Omicron wave period). To provide healthcare decision-makers the most recent trends, this study reports incidence rates (IR) and severity of first SARS-CoV-2 infection; and relative risk, healthcare utilization, and costs related to first COVID-19 hospitalizations in the full year of 2022 and overall between April 2020 and December 2022.

These updated results showed a 2.9% prevalence of immune compromise in the population. From April 2020 through December 2022, the overall IR of COVID-19 was 115.7 per 1000 patient-years in the composite IC cohort and 77.8 per 1000 patient-years in the HIRD cohort. The composite IC cohort had a 15.4% hospitalization rate with an average cost of $42,719 for first COVID-19 hospitalization. Comparatively, the HIRD cohort had a 3.7% hospitalization rate with an average cost of $28,848 for first COVID-19 hospitalization. Compared to the general population, IC individuals had 4.3 to 23 times greater risk of hospitalization with first diagnosis of COVID-19. Between January and December 2022, hospitalizations associated with first COVID-19 diagnosis cost over $1 billion, with IC individuals (~ 3% of the population) generating $310 million (31%) of these costs.

While only 2.9% of the population, IC individuals had a higher risk of COVID-19 hospitalization and incurred higher healthcare costs across variants. They also disproportionately accounted for over 30% of total costs for first COVID-19 hospitalization in 2022, amounting to ~ $310 million. These data highlight the need for additional preventive measures to decrease the risk of developing severe COVID-19 outcomes in vulnerable IC populations.

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x  to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

Whole Genome Sequencing (WGS) of the SARS-CoV-2 virus is crucial in the surveillance of the COVID-19 pandemic. Several primer schemes have been developed to sequence nearly all of the ~30,000 nucleotide SARS-CoV-2 genome, using a multiplex PCR approach to amplify cDNA copies of the viral genomic RNA. Midnight primers and ARTIC V4.1 primers are the most popular primer schemes that can amplify segments of SARS-CoV-2 (400 bp and 1200 bp, respectively) tiled across the viral RNA genome. Mutations within primer binding sites and primer-primer interactions can result in amplicon dropouts and coverage bias, yielding low-quality genomes with 'Ns' inserted in the missing amplicon regions, causing inaccurate lineage assignments, and making it challenging to monitor lineage-specific mutations in Variants of Concern (VoCs).

In this study we used a set of seven long-range PCR primer pairs to sequence clinical isolates of SARS-CoV-2 on Oxford Nanopore sequencer. These long-range primers generate seven amplicons approximately 4500 bp that covered whole genome of SARS-CoV-2. One of these regions includes the full-length S-gene by using a set of flanking primers. We also evaluated the performance of these long-range primers with Midnight primers by sequencing 94 clinical isolates in a Nanopore flow cell.

Using a small set of long-range primers to sequence SARS-CoV-2 genomes reduces the possibility of amplicon dropout and coverage bias. The key finding of this study is that long range primers can be used in single-molecule sequencing of RNA viruses in surveillance of emerging variants. We also show that by designing primers flanking the S-gene, we can obtain reliable identification of SARS-CoV-2 variants.

One of the main responsibilities of health systems impacted by the global Coronavirus disease 2019 (COVID-19) pandemic, where the first case was discovered in Wuhan, China, in December 2019, is the provision of medical services. The current study looked into the impact of vaccination on the utilization of services provided to COVID-19 patients.

This study was conducted in Iran between 2021 and 2022, utilizing a cross-sectional research design. The research team collected data on the utilization of provided services and the number of COVID-19 vaccines administered to 1000 patients in Iran through a random sampling approach. The data were analyzed with statistical methods, including the mean difference test, and multiple linear regression.

Regression estimates show that after controlling for confounding variables like age, type of admission, and comorbidities, vaccination reduces the utilization of healthcare services in the general majority of services. The study's results reveal a fall in COVID-19 patients' utilization of services, specifically in patients administered two or three doses of the vaccine. However, the reduction is not statistically significant. Regression models are in contrast to univariate analysis findings that vaccination increases the mean utilization of healthcare services for COVID-19 patients in general. Comorbidities are a crucial factor in determining the utilization of diagnostic and treatment services for COVID-19 patients.

Full COVID-19 vaccination and other implementations, including investing in public health, cooperating globally, and vaccinating high-risk groups for future pandemics, are essential as a critical response to this pandemic as they reduce healthcare service utilization to alleviate the burden on healthcare systems and allocate resources more efficiently.

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Rapid and accurate quantification of low-abundance protein biomarkers in biofluids can transform the diagnosis of a range of pathologies, including infectious diseases. Here, we harness ultrabright plasmonic fluors as "digital nanolabels" and demonstrate the detection and quantification of subfemtomolar concentrations of human IL-6 and SARS-CoV-2 alpha and variant proteins in clinical nasopharyngeal swab and saliva samples from COVID-19 patients. The resulting digital plasmonic fluor-linked immunosorbent assay (digital p-FLISA) enables detection of SARS-CoV-2 nucleocapsid protein, both in solution and in live virions. Digital p-FLISA outperforms the "gold standard" enzyme-linked immunosorbent assay (ELISA), having a nearly 7000-fold lower limit-of-detection, and outperforms a commercial antigen test, having over 5000-fold improvement in analytical sensitivity. Detection and quantification of very low concentrations of target proteins holds potential for early detection of pathological conditions, treatment monitoring, and personalized medicine.

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

Using a compartmental model parameterized to reproduce available reports of new Coronavirus Disease 2019 (COVID-19) cases, we quantified the impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2) on regional epidemics in the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix.

Timely genomic surveillance is required to inform public health responses to new SARS-CoV-2 variants. However, the processes involved in local genomic surveillance introduce inherent time constraints. The Regional Innovative Public Health Laboratory in Chicago developed and employed a genomic surveillance response playbook for the early detection and surveillance of emerging SARS-CoV-2 variants.

The playbook outlines modifications to sampling strategies, laboratory workflows, and communication processes based on the emerging variant's predicted viral characteristics, observed public health impact in other jurisdictions and local community risk level. The playbook outlines procedures for implementing and reporting enhanced and accelerated genomic surveillance, including supplementing whole genome sequencing (WGS) with variant screening by quantitative PCR (qPCR).

The ability of the playbook to improve the response to an emerging variant was tested for SARS-CoV-2 Omicron BA.1. Increased submission of clinical remnant samples from local hospital laboratories enabled detection of a new variant at an average of 1.4% prevalence with 95% confidence rather than 3.5% at baseline. Genotyping qPCR concurred with WGS lineage assignments in 99.9% of 1541 samples with results by both methods, and was more sensitive, providing lineage results in 90.4% of 1833 samples rather than 85.1% for WGS, while significantly reducing the time to lineage result.

The genomic surveillance response playbook provides a structured, stepwise, and data-driven approach to responding to emerging SARS-CoV-2 variants. These pre-defined processes can serve as a template for other genomic surveillance programs to streamline workflows and expedite the detection and public health response to emerging variants. Based on the processes piloted during the Omicron BA.1 response, this method has been applied to subsequent Omicron subvariants and can be readily applied to future SARS-CoV-2 emerging variants and other public health surveillance activities.

Since the commencement of Corona Virus Disease 2019 (COVID-19) pandemic, which has resulted in millions of mortalities globally, the efforts to minimize the damages have equally been up to the task. One of those efforts includes the mass vaccine development initiative targeting the deadly Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). So far, vaccines have tremendously decreased the rate of transmission and infection in most parts of the world. However, the repeated resurgence of different types of mutated versions of the virus, also known as variants, has somehow created uncertainties about the efficacies of different types of vaccines. This review discusses some of the interesting SARS-CoV-2 features, including general structure, genomics, and mechanisms of variants development and their consequent immune escape. This review also focuses very briefly on antigenic drift, shift, and vaccine-developing platforms.

The global pandemic caused by COVID-19 posed a significant challenge to public health, necessitating rapid scientific interventions to tackle the spread of infection. The review discusses the key areas of research on COVID-19 including viral genomics, epidemiology, pathogenesis, diagnostics, and therapeutics. The genome sequencing of the virus facilitated the tracking of its evolution, transmission dynamics, and identification of variants. Epidemiological studies have provided insights into disease spread, risk factors, and the impact of public health infrastructure and social distancing measures. Investigations of the viral pathogenesis have elucidated the mechanisms underlying immune responses and severe manifestations including the long-term effects of COVID-19. Overall, the article provides an updated overview of the diagnostic methods developed for SARS-CoV-2 and discusses their strengths, limitations, and appropriate utilization in different clinical and public health settings. Furthermore, therapeutic approaches including antiviral drugs, immunomodulatory therapies, and repurposed medications have been investigated to alleviate disease severity and improve patient outcomes. Through a comprehensive analysis of these scientific efforts, the review provides an overview of the advancements made in understanding and tackling SARS-CoV-2, while underscoring the need for continued research to address the evolving challenges posed by this global health crisis.

Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of coevolving sites in the alignment (motifs), identified via coevolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the coevolutionary coupling of pairs and manifest as coevolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness, and robustness. Lastly, we study how well each lineage is classified by such a system.

The emergence and worldwide spread of SARS-CoV-2 during the COVID-19 pandemic necessitated the adaptation and rapid deployment of viral WGS and analysis techniques that had been previously applied on a more limited basis to other viral pathogens, such as HIV and influenza viruses. The need for WGS was driven in part by the low mutation rate of SARS-CoV-2, which necessitated measuring variation along the entire genome sequence to effectively differentiate lineages and characterize viral evolution. Several WGS approaches designed to maximize throughput and accuracy were quickly adopted by surveillance labs around the world. These broad-based SARS-CoV-2 genomic sequencing efforts revealed ongoing evolution of the virus, highlighted by the successive emergence of new viral variants throughout the course of the pandemic. These genomic insights were instrumental in characterizing the effects of viral mutations on transmissibility, immune escape and viral tropism, which in turn helped guide public health policy, the use of monoclonal antibody therapeutics and vaccine development strategies. As the use of direct-acting antivirals for the treatment of COVID-19 became more widespread, the potential for emergence of antiviral resistance has driven ongoing efforts to delineate resistance mutations and to monitor global sequence databases for their emergence. Given the critical role of viral genomics in the international effort to combat the COVID-19 pandemic, coordinated efforts should be made to expand global genomic surveillance capacity and infrastructure towards the anticipation and prevention of future pandemics.

Infection with SARS-CoV-2 is a growing concern to the global well-being of the public at present. Different amino acid mutations alter the biological and epidemiological characteristics, as well as immune resistance of SARS-CoV-2. The virus-induced pulmonary impairment and inflammatory cytokine storm are directly related to its clinical manifestations. But, the fundamental mechanisms of inflammatory responses are found to be the reason for the death of immune cells which render the host immune system failure. Apoptosis of immune cells is one of the most common forms of programmed cell death induced by the virus for its survival and virulence property. ORF3a, a SARS-CoV-2 accessory viral protein, induces apoptosis in host cells and suppress the defense mechanism. This suggests, inhibiting SARS-CoV-2 ORF3a protein is a good therapeutic strategy for the treatment in COVID-19 infection by promoting the host immune defense mechanism.

Aim: Sotrovimab is an engineered human monoclonal antibody that binds a conserved region of the SARS-CoV-2 spike protein. The COMET-ICE phase III study evaluated sotrovimab for treatment of mild to moderate COVID-19 in nonhospitalized participants with ≥1 risk factor for severe disease progression. Materials & methods: We evaluated the presence of circulating SARS-CoV-2 variants of concern or interest (VOCs/VOIs) and characterized the presence of baseline, post-baseline and emergent amino acid substitutions detected in the epitope of sotrovimab in SARS-CoV-2. Results: None of the sotrovimab-treated participants with baseline epitope substitutions, and 1 of 48 sotrovimab-treated participants with post-baseline epitope substitutions, met the primary clinical endpoint for progression. Conclusion: Overall, progression was not associated with identified VOC/VOI or the presence of epitope substitutions in sotrovimab-treated participants.

The SARS-CoV-2 prefusion spike heads (receptor binding domains, RBDs) frequently nod down and up to interact with host cell receptors. As the spike protein is a trimeric unit of significant size, to understand its large-scale structural dynamics associated with the nodding mechanism and the mutational impact on the same, we develop a topological symmetry-information-loaded coarse-grained structure-based model of a spike trimer using recent cryo-EM structural data. Our study reveals the control of two distant intrinsically disordered regions (IDRs), namely, 630 and FPPR loops, over the nodding dynamics of spike heads. We find that the order-disorder transition of IDRs becomes more evident in the variants of concern (VOCs) that are associated with the characteristic mutation, D614G, in the proximity of these IDRs. In some VOCs, the two other mutations A570D and S982A also show an integral effect. The driver mutation D614G instigates a salt-bridge disruption, altering the order-disorder dynamics of both 630 and FPPR loops and their interaction with the C-terminal domains (CTD1/CTD2). This altered connectivity in these mutants allows the two IDRs to act collectively as a "cervical collar" for the RBD, supporting various spike head postures, consistent with cryo-EM results available for specific cases. The IDRs' control over the spike structure and dynamics presents an exciting opportunity where they can be targeted as remote operational switches to artificially maneuver the nod for effective therapeutic interventions.