Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics

SARS-CoV-2 Omicron XBB infections boost cross-variant neutralizing antibodies, potentially explaining the observed delay of the JN.1 wave in some Brazilian regions

Objectives: The SARS-CoV-2 JN.1 lineage emerged in late 2023 and quickly replaced the XBB lineages, becoming the predominant Omicron variant worldwide in 2024. We estimate the epidemiologic impact of this SARS-CoV-2 lineage replacement in Brazil and we further assessed the cross-reactive neutralizing antibody (NAb) responses in a cohort of convalescent Brazilian patients infected during 2023.

Methods

We analyzed the evolution of SARS-CoV-2 lineages and severe acute respiratory infection (SARI) cases in Brazil between July 2023 and March 2024. We evaluated the cross-reactive NAb responses to the JN.1 variant in a cohort of convalescent Brazilian patients before and after infection with XBB.1* lineages.

Results

JN.1 replaced XBB with similar temporal dynamics across all country regions, although its epidemiologic impact varied between locations. The southeastern, southern, and central-western regions experienced a brief XBB wave around October 2023, shortly before the introduction of JN.1, without any immediate upsurge of SARI cases during viral lineage replacement. By contrast, the northeastern and northern regions did not experience an XBB wave in the latter half of 2023 and displayed a rapid surge in SARI cases driven by the emergence of the JN.1. We found that recent XBB infections in the Brazilian population significantly boosted cross-reactive NAb levels against JN.1.

Conclusion

The XBB wave observed in the second half of 2023 in some Brazilian states likely acted as a booster for population immunity, providing short-term protection against JN.1 infections and delaying the rise of SARI cases in certain regions of the country.

Microbial Dynamics in COVID-19: Unraveling the Impact of Human Microbiome on Disease Susceptibility and Therapeutic Strategies

This review explores the bidirectional relationship between the human microbiome and SARS-CoV-2 infection, elucidating its implications for COVID-19 susceptibility, severity, and therapeutic strategies. Metagenomic analyses reveal notable alterations in microbiome composition associated with SARS-CoV-2 infection, impacting disease severity and clinical outcomes. Dysbiosis within the respiratory, gastrointestinal, oral, and skin microbiomes exacerbates COVID-19 pathology through immune dysregulation and inflammatory pathways. Understanding these microbial shifts is pivotal for devising targeted therapeutic interventions. Notably, co-infection of oral pathogens with SARS-CoV-2 worsens lung pathology, while gut microbiome dysbiosis influences viral susceptibility and severity. Potential therapeutic approaches targeting the microbiome include probiotics, antimicrobial agents, and immunomodulatory strategies. This review underscores the importance of elucidating host-microbiota interactions to advance precision medicine and public health initiatives in combating COVID-19 and other infectious diseases.

Beyond six feet: The collective behavior of social distancing

In a severe epidemic such as the COVID-19 pandemic, social distancing can be a vital tool to stop the spread of the disease and save lives. However, social distancing may induce profound negative social or economic impacts as well. How to optimize social distancing is a serious social, political, as well as public health issue yet to be resolved. This work investigates social distancing with a focus on how every individual reacts to an epidemic, what role he/she plays in social distancing, and how every individual's decision contributes to the action of the population and vice versa. Social distancing is thus modeled as a population game, where every individual makes decision on how to participate in a set of social activities, some with higher frequencies while others lower or completely avoided, to minimize his/her social contacts with least possible social or economic costs. An optimal distancing strategy is then obtained when the game reaches an equilibrium. The game is simulated with various realistic restraints including (i) when the population is distributed over a social network, and the decision of each individual is made through the interactions with his/her social neighbors; (ii) when the individuals in different social groups such as children vs. adults or the vaccinated vs. unprotected have different distancing preferences; (iii) when leadership plays a role in decision making, with a certain number of leaders making decisions while the rest of the population just follow. The simulation results show how the distancing game is played out in each of these scenarios, reveal the conflicting yet cooperative nature of social distancing, and shed lights on a self-organizing, bottom-up perspective of distancing practices.

Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment.

Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates

BACKGROUND

Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates.

METHODS

We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( S ), vaccinated ( V ), variant-specific infected (I 1 andI 2 ), recovered ( R ), and seropositive ( T ) model ( S V I 2 R T ) tracked prevalence longitudinally. This was related to wastewater concentration.

RESULTS

Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag.

CONCLUSIONS

Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Introduction

The COVID-19 pandemic continues with highly contagious variants and waning immunity. As the virus keeps evolving to be more infectious and immune evasive, some question whether the COVID-19 pandemic can be managed through sustainable public health measures.

Methods

We developed an agent-based simulation to explore the impact of COVID-19 mutations, periodic vaccinations, and nonpharmaceutical interventions on reducing COVID-19 deaths. The model is calibrated to the greater Seattle area by observing local epidemic data. We perform scenario analyses on viral mutations that change infectiousness, disease severity, and immune evasiveness from previous infections and vaccination every 6 months. The simulation is run until the end of year 2023.

Results

Variants with increased infectivity or increased immune evasion dominate previous strains. With enhanced immune protection from a pancoronavirus vaccine, the most optimistic periodic vaccination rate reduces average total deaths by 44.6% compared with the most pessimistic periodic vaccination rate. A strict threshold nonpharmaceutical intervention policy reduces average total deaths by 71.3% compared with an open society, whereas a moderate nonpharmaceutical intervention policy results in a 33.6% reduction.

Conclusions

Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

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).

METHODOLOGY

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.

FINDINGS

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.

INTERPRETATION

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.

Co-evolution of SARS-CoV-2 variants and host immune response trajectories underlie COVID-19 pandemic to epidemic transition

COVID-19 pandemic saw emergence of multiple SAR-CoV-2 variants. Exacerbated risk of severe outcome and hospital admissions led us to comprehend differential host-immune kinetics associated with SARS-CoV-2 variants. Longitudinal investigation was conducted through different time periods of Pre-VOC and VOCs (Delta & Omicron) mapping host transcriptome features. Robust antiviral type-1 interferon response marked Omicron infection, which was largely missing during Pre-VOC and Delta waves. SARS-CoV-2-host protein-protein interactions and docking complexes highlighted N protein to interact with HNRNPA1 in Pre-VOC, demonstrating its functional role for enhanced viral replication. Omicron revealed enhanced binding efficiency of LARP1 to N protein, probably potentiating antiviral effects of LARP1. Differential expression of zinc finger protein genes, especially in Omicron, mechanistically support induction of strong IFN (Interferon) response, thereby strengthening early viral clearance. Study highlights eventual adaptation of host to immune activation patterns that interrupt virus evolution with enhanced immune-evasion mutations and counteraction mechanisms, delimiting the next phase of COVID-19 pandemic.

ISG15: its roles in SARS-CoV-2 and other viral infections

Interferon (IFN)-stimulated gene 15 (ISG15), a ubiquitin-like pleiotropic protein and one of the most abundant ISGs, has been studied extensively; however, its roles in SARS-CoV-2 and other viral infections have just begun to be elucidated. Emerging evidence suggests that ISG15 - either in its conjugated or unconjugated 'free' form - acts both intracellularly and extracellularly, and exerts anti- or pro-viral effects. To counteract ISG15's antiviral roles, viruses have evolved sophisticated tactics. Here, we discuss recent advances in ISG15's physiological functions as a post-translational modifier or 'cytokine-like' molecule during SARS-CoV-2 and other viral infections. Furthermore, we highlight the detailed mechanisms viruses use to block ISG15-dependent antiviral defenses. A comprehensive understanding of ISG15 biology in the context of virus infection may spur new therapeutic approaches for a range of viral infectious diseases.

Fangchinoline inhibits SARS-CoV-2 and MERS-CoV entry

The COVID-19 pandemic caused by SARS-CoV-2, lead to mild to severe respiratory illness and resulted in 6.9 million deaths worldwide. Although vaccines are effective in preventing COVID-19, they may not be sufficient to protect immunocompromised individuals from this respiratory illness. Moreover, novel emerging variants of SARS-CoV-2 pose a risk of new COVID-19 waves. Therefore, identification of effective antivirals is critical in controlling SARS and other coronaviruses, such as MERS-CoV. We show that Fangchinoline (Fcn), a bisbenzylisoquinoline alkaloid, inhibits replication of SARS-CoV, SARS-CoV-2, and MERS-CoV in a range of in vitro assays, by blocking entry. Therapeutic use of Fcn inhibited viral loads in the lungs, and suppressed associated airway inflammation in hACE2. Tg mice and Syrian hamster infected with SARS-CoV-2. Combination of Fcn with remdesivir (RDV) or an anti-leprosy drug, Clofazimine, exhibited synergistic antiviral activity. Compared to Fcn, its synthetic derivative, MK-04-003, more effectively inhibited SARS-CoV-2 and its variants B.1.617.2 and BA.5 in mice. Taken together these data demonstrate that Fcn is a pan beta coronavirus inhibitor, which possibly can be used to combat novel emerging coronavirus diseases.

Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study

In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.

Fitness, growth and transmissibility of SARS-CoV-2 genetic variants

The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.

Longitudinal kinetics of neutralizing antibodies against circulating SARS-CoV-2 variants and estimated level of group immunity of booster-vaccinated individuals during omicron-dominated COVID-19 outbreaks in the Republic of Korea, 2022

The coronavirus disease 2019 pandemic persisted for 3 years and is now transitioning to endemicity. We illustrated the change in group immunity induced by vaccination (monovalent vaccines) and breakthrough infections (BIs) in a healthcare worker (HCW) cohort. Five sampling points were analyzed: before the third dose and 1, 3, 5, and 8 months after the vaccination. The last two points corresponded roughly to 1 and 4 months after omicron BA.1/BA.2 BI. A semi-quantitative anti-spike binding antibody (Sab) assay and plaque reduction neutralization test (PRNT) against circulating variants were conducted. A linear regression model was utilized to deduce correlation equations. Baseline characteristics and antibody titers after the third dose were not different between 106 HCWs with or without BI (54/52). One month after the third dose, BA.1 PRNT increased with wild-type (WT), but 3 months after the third dose, it decreased more rapidly than WT PRNT. After BI, BA.1 PRNT increased robustly and waned slower than WT. A linear equation of waning kinetics was deduced between log10Sab and months, and the slope became gradual after BI. The estimated BA.5 PRNT titers at the beginning of the BA.5 outbreak were significantly higher than the BA.1 PRNT titers of the initial BA.1/BA.2 wave, which might be associated with the smaller size of the BA.5 wave. BA.1/BA.2 BI after the third dose elicited robust and broad neutralizing activity, preferentially maintaining cross-neutralizing longevity against BA.1 and BA.5. The estimated kinetics provide an overview of group immunity through the third vaccination and BA.1/BA.2 BI, correlating with the actual outbreaks. IMPORTANCE This study analyzed changes in group immunity induced by coronavirus disease 2019 (COVID-19) vaccination and BA.1/BA.2 breakthrough infections (BIs) in a healthcare worker cohort. We investigated the longitudinal kinetics of neutralizing antibodies against circulating variants and confirmed that BA.1/BA.2 BIs enhance the magnitude and durability of cross-neutralization against BA.1 and BA.5. Correlation equations between semi-quantitative anti-spike antibody and plaque reduction neutralization test titers were deduced from the measured values using a linear regression model. Based on the equations, group immunity was estimated to last up to 11 months following the third dose of the COVID-19 vaccine. The estimated group immunity suggests that the augmented immunity and flattened waning slope through BI could correlate with the overall outbreak size. Our findings could provide a better understanding to establish public health strategies against future endemicity.

The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with 'lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.

Frequency of Atypical Mutations in the Spike Glycoprotein in SARS-CoV-2 Circulating from July 2020 to July 2022 in Central Italy: A Refined Analysis by Next Generation Sequencing

In this study, we provided a retrospective overview in order to better define SARS-CoV-2 variants circulating in Italy during the first two years of the pandemic, by characterizing the spike mutational profiles and their association with viral load (expressed as ct values), N-glycosylation pattern, hospitalization and vaccination. Next-generation sequencing (NGS) data were obtained from 607 individuals (among them, 298 vaccinated and/or 199 hospitalized). Different rates of hospitalization were observed over time and among variants of concern (VOCs), both in the overall population and in vaccinated individuals (Alpha: 40.7% and 31.3%, Beta: 0%, Gamma: 36.5% and 44.4%, Delta: 37.8% and 40.2% and Omicron: 11.2% and 7.1%, respectively, both p-values < 0.001). Approximately 32% of VOC-infected individuals showed at least one atypical major spike mutation (intra-prevalence > 90%), with a distribution differing among the strains (22.9% in Alpha, 14.3% in Beta, 41.8% in Gamma, 46.5% in Delta and 15.4% in Omicron, p-value < 0.001). Overall, significantly less atypical variability was observed in vaccinated individuals than unvaccinated individuals; nevertheless, vaccinated people who needed hospitalization showed an increase in atypical variability compared to vaccinated people that did not need hospitalization. Only 5/607 samples showed a different putative N-glycosylation pattern, four within the Delta VOC and one within the Omicron BA.2.52 sublineage. Interestingly, atypical minor mutations (intra-prevalence < 20%) were associated with higher Ct values and a longer duration of infection. Our study reports updated information on the temporal circulation of SARS-CoV-2 variants circulating in Central Italy and their association with hospitalization and vaccination. The results underline how SARS-CoV-2 has changed over time and how the vaccination strategy has contributed to reducing severity and hospitalization for this infection in Italy.

Immune responses in COVID-19 patients during breakthrough infection with SARS-CoV-2 variants Delta, Omicron-BA.1 and Omicron-BA.5

Background

Breakthrough infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are increasingly observed in vaccinated individuals. Immune responses towards SARS-CoV-2 variants, particularly Omicron-BA.5, are poorly understood. We investigated the humoral and cellular immune responses of hospitalized COVID-19 patients during Delta and Omicron infection waves.

Methods

The corresponding SARS-CoV-2 variant of the respective patients were identified by whole genome sequencing. Humoral immune responses were analyzed by ELISA and a cell culture-based neutralization assay against SARS-CoV-2 D614G isolate (wildtype), Alpha, Delta (AY.43) and Omicron (BA.1 and BA.5). Cellular immunity was evaluated with an IFN-γ ELISpot assay.

Results

On a cellular level, patients showed a minor IFN-γ response after stimulating PBMCs with mutated regions of SARS-CoV-2 variants. Neutralizing antibody titers against Omicron-BA.1 and especially BA.5 were strongly reduced. Double-vaccinated patients with Delta breakthrough infection showed a significantly increased neutralizing antibody response against Delta compared to double-vaccinated uninfected controls (median complete neutralization titer (NT₁₀₀) 640 versus 80, p<0.05). Omicron-BA.1 infection increased neutralization titers against BA.1 in double-vaccinated patients (median NT₁₀₀ of 160 in patients versus 20 in controls, p=0.07) and patients that received booster vaccination (median NT₁₀₀ of 50 in patients versus 20 in controls, p=0.68). For boosted patients with BA.5 breakthrough infection, we found no enhancing effect on humoral immunity against SARS-CoV-2 variants.

Conclusion

Neutralizing antibody titers against Omicron-BA.1 and especially BA.5 were strongly reduced in SARS-CoV-2 breakthrough infections. Delta and Omicron-BA.1 but not Omicron-BA.5 infections boosted the humoral immunity in double-vaccinated patients and patients with booster vaccination. Despite BA.5 breakthrough infection, those patients may still be vulnerable for reinfections with BA.5 or other newly emerging variants of concern.

The Impact of SARS-CoV-2 Lineages (Variants) and COVID-19 Vaccination on the COVID-19 Epidemic in South Africa: Regression Study

Background

Emerging SARS-CoV-2 variants have been attributed to the occurrence of secondary, tertiary, quaternary, and quinary COVID-19 epidemic waves threatening vaccine efforts owing to their immune invasiveness. Since the importation of SARS-CoV-2 in South Africa, with the first reported COVID-19 case on March 5, 2020, South Africa has observed 5 consecutive COVID-19 epidemic waves. The evolution of SARS-CoV-2 has played a major role in the resurgence of COVID-19 epidemic waves in South Africa and across the globe.

Objective

We aimed to conduct descriptive and inferential statistical analysis on South African COVID-19 epidemiological data to investigate the impact of SARS-CoV-2 lineages and COVID-19 vaccinations in South African COVID-19 epidemiology.

Methods

The general methodology involved the collation and stratification, covariance, regression analysis, normalization, and comparative inferential statistical analysis through null hypothesis testing (paired 2-tailed t tests) of South African COVID-19 epidemiological data.

Results

The mean daily positive COVID-19 tests in South Africa's first, second, third, fourth, and fifth COVID-19 epidemic wave periods were 11.5% (SD 8.58%), 11.5% (SD 8.45%), 13.3% (SD 9.72%), 13.1% (SD 9.91%), and 14.3% (SD 8.49%), respectively. The COVID-19 transmission rate in the first and second COVID-19 epidemic waves in South Africa was similar, while the COVID-19 transmission rate was higher in the third, fourth, and fifth COVID-19 epidemic waves than in the aforementioned waves. Most COVID-19 hospitalized cases in South Africa were in the general ward (60%-79.1%). Patients with COVID-19 on oxygen were the second-largest admission status (11.2%-16.8%), followed by patients with COVID-19 in the intensive care unit (8.07%-16.7%). Most patients hospitalized owing to COVID-19 in South Africa's first, second, third, and fourth COVID-19 epidemic waves were aged between 40 and 49 years (16.8%-20.4%) and 50 and 59 years (19.8%-25.3%). Patients admitted to the hospital owing to COVID-19 in the age groups of 0 to 19 years were relatively low (1.98%-4.59%). In general, COVID-19 hospital admissions in South Africa for the age groups between 0 and 29 years increased after each consecutive COVID-19 epidemic wave, while for age groups between 30 and 79 years, hospital admissions decreased. Most COVID-19 hospitalization deaths in South Africa in the first, second, third, fourth, and fifth COVID-19 epidemic waves were in the ages of 50 to 59 years (15.8%-24.8%), 60 to 69 years (15.9%-29.5%), and 70 to 79 years (16.6%-20.7%).

Conclusions

The relaxation of COVID-19 nonpharmaceutical intervention health policies in South Africa and the evolution of SARS-CoV-2 were associated with increased COVID-19 transmission and severity in the South African population. COVID-19 vaccination in South Africa was strongly associated with a decrease in COVID-19 hospitalization and severity in South Africa.

Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.

Assessment of risk scores to predict mortality of COVID-19 patients admitted to the intensive care unit

Objectives

To assess the ABC2-SPH score in predicting COVID-19 in-hospital mortality, during intensive care unit (ICU) admission, and to compare its performance with other scores (SOFA, SAPS-3, NEWS2, 4C Mortality Score, SOARS, CURB-65, modified CHA2DS2-VASc, and a novel severity score).

Materials and methods

Consecutive patients (≥ 18 years) with laboratory-confirmed COVID-19 admitted to ICUs of 25 hospitals, located in 17 Brazilian cities, from October 2020 to March 2022, were included. Overall performance of the scores was evaluated using the Brier score. ABC2-SPH was used as the reference score, and comparisons between ABC2-SPH and the other scores were performed by using the Bonferroni method of correction. The primary outcome was in-hospital mortality.

Results

ABC2-SPH had an area under the curve of 0.716 (95% CI 0.693-0.738), significantly higher than CURB-65, SOFA, NEWS2, SOARS, and modified CHA2DS2-VASc scores. There was no statistically significant difference between ABC2-SPH and SAPS-3, 4C Mortality Score, and the novel severity score.

Conclusion

ABC2-SPH was superior to other risk scores, but it still did not demonstrate an excellent predictive ability for mortality in critically ill COVID-19 patients. Our results indicate the need to develop a new score, for this subset of patients.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Hospital antibiotic consumption-an interrupted time series analysis of the early and late phases of the COVID-19 pandemic in Poland, a retrospective study

BACKGROUND

COVID-19 has been challenging for the entire healthcare system, due to the lack of sufficient treatment protocols, especially during initial phases and as regards antibiotic use. The aim of this study was to identify the trends of antimicrobial consumption in one of the largest tertiary hospitals in Poland during COVID-19.

METHODS

This is a retrospective study conducted at the University Hospital in Krakow, Poland, between Feb/Mar 2020 and Feb 2021. It included 250 patients. All included patients were hospitalized due to COVID-19 with confirmed SARS-CoV-2 infection without bacterial co-infections during the first phase of COVID-19 in Europe and following 3-month intervals: five equal groups of patients in each. COVID severity and antibiotic consumption were assessed according to WHO recommendations.

RESULTS

In total 178 (71.2%) patients received antibiotics with a incidence rate of laboratory-confirmed healthcare-associated infection (LC-HAI) was 20%. The severity of COVID-19 was mild in 40.8%, moderate in 36.8%, and severe in 22.4% cases. The ABX administration was significantly higher for intensive care unit (ICU) patients (97.7% vs. 65.7%). Length of hospital stay was extended for patients with ABX (22.3 vs. 14.4 days). In total, 3 946.87 DDDs of ABXs were used, including 1512.63 DDDs in ICU, accounting for 780.94 and 2522.73 per 1000 hospital days, respectively. The median values of antibiotic DDD were greater among patients with severe COVID-19 than others (20.92). Patients admitted at the beginning of the pandemic (Feb/Mar, May 2020) had significantly greater values of median DDDs, respectively, 25.3 and 16.0 compared to those admitted in later (Aug, Nov 2020; Feb 2021), respectively, 11.0, 11.0 and 11.2, but the proportion of patients receiving ABX therapy was lower in Feb/Mar and May 2020 (62.0 and 48.0%), whereas the highest during the late period of the pandemic, i.e., in Aug, Nov. 2020 and Feb. 2021 (78% and both 84.0%).

CONCLUSIONS

Data suggest great misuse of antibiotics without relevant data about HAIs. Almost all ICU patients received some antibiotics, which was correlated with prolonged hospitalization.

COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations

COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals.

Cost-effectiveness of future lockdown policies against the COVID-19 pandemic

AIM

While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease.

METHODS

A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios-a booster shot's efficacy of 0%, 50%, and 95%.

RESULTS

The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors).

CONCLUSION

In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%.

Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower.

Modelling the impact of the Omicron BA.5 subvariant in New Zealand

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

The impact of cross-reactive immunity on the emergence of SARS-CoV-2 variants

Introduction

A key feature of the COVID-19 pandemic has been the emergence of SARS-CoV-2 variants with different transmission characteristics. However, when a novel variant arrives in a host population, it will not necessarily lead to many cases. Instead, it may fade out, due to stochastic effects and the level of immunity in the population. Immunity against novel SARS-CoV-2 variants may be influenced by prior exposures to related viruses, such as other SARS-CoV-2 variants and seasonal coronaviruses, and the level of cross-reactive immunity conferred by those exposures.

Methods

Here, we investigate the impact of cross-reactive immunity on the emergence of SARS-CoV-2 variants in a simplified scenario in which a novel SARS-CoV-2 variant is introduced after an antigenically related virus has spread in the population. We use mathematical modelling to explore the risk that the novel variant invades the population and causes a large number of cases, as opposed to fading out with few cases.

Results

We find that, if cross-reactive immunity is complete (i.e. someone infected by the previously circulating virus is not susceptible to the novel variant), the novel variant must be more transmissible than the previous virus to invade the population. However, in a more realistic scenario in which cross-reactive immunity is partial, we show that it is possible for novel variants to invade, even if they are less transmissible than previously circulating viruses. This is because partial cross-reactive immunity effectively increases the pool of susceptible hosts that are available to the novel variant compared to complete cross-reactive immunity. Furthermore, if previous infection with the antigenically related virus assists the establishment of infection with the novel variant, as has been proposed following some experimental studies, then even variants with very limited transmissibility are able to invade the host population.

Discussion

Our results highlight that fast assessment of the level of cross-reactive immunity conferred by related viruses against novel SARS-CoV-2 variants is an essential component of novel variant risk assessments.

Patterns of reported infection and reinfection of SARS-CoV-2 in England

One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Assessing the Impact of COVID-19 on the Health of Native Hawaiian/Pacific Islander People in the United States, 2021

OBJECTIVES

Minimal research has assessed COVID-19's unique impact on the Native Hawaiian/Pacific Islander (NH/PI) population-an Indigenous-colonized racial group with social and health disparities that increase their risk for COVID-19 morbidity and mortality. To address this gap, we explored the scope of COVID-19 outcomes, vaccination status, and health in diverse NH/PI communities.

METHODS

NH/PI staff at partner organizations collected survey data from April through November 2021 from 319 community-dwelling NH/PI adults in 5 states with large NH/PI populations: Arkansas, California, Oregon, Utah, and Washington. Data were analyzed with descriptive statistics, Pearson χ2 tests, independent and paired t tests, and linear and logistic regression analyses.

RESULTS

During the COVID-19 pandemic, 30% of survey participants had contracted COVID-19, 16% had a close family member who died of the disease, and 64% reported COVID-19 vaccine uptake. Thirty percent reported fair/poor health, 21% currently smoked cigarettes, and 58% reported obesity. Survey participants reported heightened COVID-19-related psychosocial distress (mean score = 4.9 on 10-point scale), which was more likely when health outcomes (general health, sleep, obesity) were poor or a family member had died of COVID-19. Logistic regression indicated that age, experiencing COVID-19 distress, and past-year use of influenza vaccines were associated with higher odds of COVID-19 vaccine uptake (1.06, 1.18, and 7.58 times, respectively).

CONCLUSIONS

Our empirical findings highlight the acute and understudied negative impact of COVID-19 on NH/PI communities in the United States and suggest new avenues for improving NH/PI community health, vaccination, and recovery from COVID-19.

Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19

The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.

Sensitivity of Detection and Variant Typing of SARS-CoV-2 in European Laboratories

The molecular detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is key for clinical management and surveillance. Funded by the European Centre for Disease Prevention and Control, we conducted an external quality assessment (EQA) on the molecular detection and variant typing of SARS-CoV-2 that included 59 European laboratories in 34 countries. The EQA panel consisted of 12 lyophilized inactivated samples, 10 of which were SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Epsilon, Eta, parental B.1 strain) ranging from 2.5 to 290.0 copies/μL or pooled respiratory viruses (adenovirus, enterovirus, influenza virus A, respiratory syncytial virus, or human coronaviruses 229E and OC43). Of all participants, 72.9% identified the presence of SARS-CoV-2 RNA correctly. In samples containing 25.0 or more genome copies/μL, SARS-CoV-2 was detected by 98.3% of the participating laboratories. Laboratories applying commercial tests scored significantly better (P < 0.0001, Kruskal-Wallis test) than those using in-house assays. Both the molecular detection and the typing of the SARS-CoV-2 variants were associated with the RNA concentrations (P < 0.0001, Kruskal-Wallis test). On average, only 5 out of the 10 samples containing different SARS-CoV-2 variants at different concentrations were correctly typed. The identification of SARS-CoV-2 variants was significantly more successful among EQA participants who combined real-time reverse transcription polymerase chain reaction (RT-PCR)-based assays for mutation detection and high-throughput genomic sequencing than among those who used a single methodological approach (P = 0.0345, Kruskal-Wallis test). Our data highlight the high sensitivity of SARS-CoV-2 detection in expert laboratories as well as the importance of continuous assay development and the benefits of combining different methodologies for accurate SARS-CoV-2 variant typing.

SARS-CoV-2 Infection in Patients with Cystic Fibrosis: What We Know So Far

Respiratory infections are the most common and most frequent diseases, especially in children and the elderly, characterized by a clear seasonality and with an incidence that usually tends to decrease with increasing age. These infections often resolve spontaneously, usually without the need for antibiotic treatment and/or with the possible use of symptomatic treatments aimed at reducing overproduction of mucus and decreasing coughing. However, when these infections occur in patients with weakened immune systems and/or underlying health conditions, their impact can become dramatic and in some cases life threatening. The rapid worldwide spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection has caused concern for everyone, becoming especially important for individuals with underlying lung diseases, such as CF patients, who have always paid close attention to implementing protective strategies to avoid infection. However, adult and pediatric CF patients contract coronavirus infection like everyone else. In addition, although numerous studies were published during the first wave of the pandemic on the risk for patients with cystic fibrosis (CF) to develop severe manifestations when infected with SARS-CoV-2, to date, a high risk has been found only for patients with poorer lung function and post-transplant status. In terms of preventive measures, vaccination remains key. The best protection for these patients is to strengthen preventive measures, such as social distancing and the use of masks. In this review, we aim to summarize and discuss recent advances in understanding the susceptibility of CF individuals to SARS-CoV-2 infection.

Assessing the transition of COVID-19 burden towards the young population while vaccines are rolled out in China

SARS-CoV-2 infection causes most cases of severe illness and fatality in older age groups. Over 92% of the Chinese population aged ≥12 years has been fully vaccinated against COVID-19 (albeit with vaccines developed against historical lineages). At the end of October 2021, the vaccination programme has been extended to children aged 3-11 years. Here, we aim to assess whether, in this vaccination landscape, the importation of Delta variant infections could shift COVID-19 burden from adults to children. We developed an age-structured susceptible-infectious-removed model of SARS-CoV-2 transmission to simulate epidemics triggered by the importation of Delta variant infections and project the age-specific incidence of SARS-CoV-2 infections, cases, hospitalizations, intensive care unit admissions, and deaths. In the context of the vaccination programme targeting individuals aged ≥12 years, and in the absence of non-pharmaceutical interventions, the importation of Delta variant infections could have led to widespread transmission and substantial disease burden in mainland China, even with vaccination coverage as high as 89% across the eligible age groups. Extending the vaccination roll-out to include children aged 3-11 years (as it was the case since the end of October 2021) is estimated to dramatically decrease the burden of symptomatic infections and hospitalizations within this age group (39% and 68%, respectively, when considering a vaccination coverage of 87%), but would have a low impact on protecting infants. Our findings highlight the importance of including children among the target population and the need to strengthen vaccination efforts by increasing vaccine effectiveness.

Fibrinogen, Fibrin, and Fibrin Degradation Products in COVID-19

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the highly pathogenic and highly transmissible human coronavirus that is the causative agent for the worldwide COVID-19 pandemic. COVID-19 manifests predominantly as a respiratory illness with symptoms consistent with viral pneumonia, but other organ systems (e.g., kidney, heart, brain) can also become perturbed in COVID-19 patients. Accumulating data suggest that significant activation of the hemostatic system is a common pathological manifestation of SARS-CoV-2 infection. The clotting protein fibrinogen is one of the most abundant plasma proteins. Following activation of coagulation, the central coagulation protease thrombin converts fibrinogen to fibrin monomers, which selfassemble to form a matrix, the primary structural component of the blood clot. Severe COVID-19 is associated with a profound perturbation of circulating fibrinogen, intra- and extravascular fibrin deposition and persistence, and fibrin degradation. Current findings suggest high levels of fibrinogen and the fibrin degradation product D-dimer are biomarkers of poor prognosis in COVID-19. Moreover, emerging studies with in vitro and animal models indicate fibrin(ogen) as an active player in COVID-19 pathogenesis. Here, we review the current literature regarding fibrin(ogen) and COVID-19, including possible pathogenic mechanisms and treatment strategies centered on clotting and fibrin(ogen) function.

Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F₁ = 0.899).

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

INTRODUCTION

Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.

OBJECTIVES

The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.

METHODS

This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.

RESULTS

Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).

CONCLUSION

An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

Modeling COVID-19 transmission between age groups in the United States considering virus mutations, vaccinations, and reinfection

The in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic's detrimental effects. We developed the SIRDV-Virulence (Susceptible-Infected-Recovered-Dead-Vaccinated-Virulence) epidemiological model based on a population balance equation to study the effects virus mutants, vaccination strategies, 'Anti/Non Vaxxer' proportions, and reinfection rates to provide methods to mitigate COVID-19 transmission among the United States population. Based on publicly available data, we obtain the key parameters governing the spread of the pandemic. The results show that a large fraction of infected cases comes from the adult and children populations in the presence of a highly infectious COVID-19 mutant. Given the situation at the end of July 2021, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of 'Anti/Non-vaxxers' and a higher reinfection rate can both independently lead to the resurgence of the pandemic.

Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model

BACKGROUND

SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France.

METHODS

In this paper we investigate the spreading potential of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants' transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations.

RESULTS

Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and an omicron generation time of 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, [Formula: see text], was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led to [Formula: see text] ranging from 1.57 to 2.34 on average over the metropolitan French regions, weighted by population size.

CONCLUSIONS

Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events.

The potential of genomics for infectious disease forecasting

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Molecular aspects of Omicron, vaccine development, and recombinant strain XE: A review

The global pandemic of COVID-19 began in December 2019 and is still continuing. The past 2 years have seen the emergence of several variants that were more vicious than each other. The emergence of Omicron (B.1.1.529) proved to be a huge epidemiological concern as the rate of infection of this particular strain was enormous. The strain was identified in South Africa on November 24, 2021 and was classified as a "Variant of Concern" on November 26, 2021. The Omicron variant possessed mutations in the key RBD region, the S region, thereby increasing the affinity of ACE2 for better transmission of the virus. Antibody resistance was found in this variant and it was able to reduce vaccine efficiency of vaccines. The need for a booster vaccine was brought forth due to the prevalence of the Omicron variant and, subsequently, this led to targeted research and development of variant-specific vaccines and booster dosage. This review discusses broadly the genomic characters and features of Omicron along with its specific mutations, evolution, antibody resistance, and evasion, utilization of CRISPR-Cas12a assay for Omicron detection, T-cell immunity elicited by vaccines against Omicron, and strategies to decrease Omicron infection along with COVID-19 and it also discusses on XE recombinant variant and on infectivity of BA.2 subvariant of Omicron.

The African swine fever modelling challenge: Model comparison and lessons learnt

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.

Comparison of the 2021 COVID-19 roadmap projections against public health data in England

Control and mitigation of the COVID-19 pandemic in England has relied on a combination of vaccination and non-pharmaceutical interventions (NPIs). Some of these NPIs are extremely costly (economically and socially), so it was important to relax these promptly without overwhelming already burdened health services. The eventual policy was a Roadmap of four relaxation steps throughout 2021, taking England from lock-down to the cessation of all restrictions on social interaction. In a series of six Roadmap documents generated throughout 2021, models assessed the potential risk of each relaxation step. Here we show that the model projections generated a reliable estimation of medium-term hospital admission trends, with the data points up to September 2021 generally lying within our 95% prediction intervals. The greatest uncertainties in the modelled scenarios came from vaccine efficacy estimates against novel variants, and from assumptions about human behaviour in the face of changing restrictions and risk.

Perspective Chapter: Tracking Trails of SARS CoV-2 - Variants to Therapy

A virus when replicates itself from one generation to another, tends to change a little bit of its structure. These variations are called mutations. History says that SARS CoV-2 originated from the virus reservoirs of animals, specifically non-human mammals like bats and minks. Since then, there are evolutionary changes in its genome due to recombination in divergent strains of different species. Thus, making the virus more robust and smarter to sustain and evade immune responses in humans. Probably, this has led to the 2019 SARS CoV-2 pandemic. This chapter tracks the evolutionary trails of the virus origin, its pathogenesis in humans, and varying variants with the coming times. Eventually, the chapter overviews the available vaccines and therapies to be followed for SARS CoV-2.

Dengue and COVID-19: two sides of the same coin

BACKGROUND

Many countries in Asia and Latin America are currently facing a double burden of outbreaks due to dengue and COVID-19. Here we discuss the similarities and differences between the two infections so that lessons learnt so far from studying both infections will be helpful in further understanding their immunopathogenesis and to develop therapeutic interventions.

MAIN BODY

Although the entry routes of the SARS-CoV-2 and the dengue virus (DENV) are different, both infections result in a systemic infection, with some similar clinical presentations such as fever, headache, myalgia and gastrointestinal symptoms. However, while dengue is usually associated with a tendency to bleed, development of micro and macrothrombi is a hallmark of severe COVID-19. Apart from the initial similarities in the clinical presentation, there are further similarities between such as risk factors for development of severe illness, cytokine storms, endothelial dysfunction and multi-organ failure. Both infections are characterised by a delayed and impaired type I IFN response and a proinflammatory immune response. Furthermore, while high levels of potent neutralising antibodies are associated with protection, poorly neutralising and cross-reactive antibodies have been proposed to lead to immunopathology by different mechanisms, associated with an exaggerated plasmablast response. The virus specific T cell responses are also shown to be delayed in those who develop severe illness, while varying degrees of endothelial dysfunction leads to increased vascular permeability and coagulation abnormalities.

CONCLUSION

While there are many similarities between dengue and SARS-CoV-2 infection, there are also key differences especially in long-term disease sequelae. Therefore, it would be important to study the parallels between the immunopathogenesis of both infections for development of more effective vaccines and therapeutic interventions.

Comparison of Patients Infected With Delta Versus Omicron COVID-19 Variants Presenting to Paris Emergency Departments : A Retrospective Cohort Study

BACKGROUND

At the end of 2021, the B.1.1.529 SARS-CoV-2 variant (Omicron) wave superseded the B.1.617.2 variant (Delta) wave.

OBJECTIVE

To compare baseline characteristics and in-hospital outcomes of patients with SARS-CoV-2 infection with the Delta variant versus the Omicron variant in the emergency department (ED).

DESIGN

Retrospective chart reviews.

SETTING

13 adult EDs in academic hospitals in the Paris area from 29 November 2021 to 10 January 2022.

PATIENTS

Patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test result for SARS-CoV-2 and variant identification.

MEASUREMENTS

Main outcome measures were baseline clinical and biological characteristics at ED presentation, intensive care unit (ICU) admission, mechanical ventilation, and in-hospital mortality.

RESULTS

A total of 3728 patients had a positive RT-PCR test result for SARS-CoV-2 during the study period; 1716 patients who had a variant determination (818 Delta and 898 Omicron) were included. Median age was 58 years, and 49% were women. Patients infected with the Omicron variant were younger (54 vs. 62 years; difference, 8.0 years [95% CI, 4.6 to 11.4 years]), had a lower rate of obesity (8.0% vs. 12.5%; difference, 4.5 percentage points [CI, 1.5 to 7.5 percentage points]), were more vaccinated (65% vs. 39% for 1 dose and 22% vs. 11% for 3 doses), had a lower rate of dyspnea (26% vs. 50%; difference, 23.6 percentage points [CI, 19.0 to 28.2 percentage points]), and had a higher rate of discharge home from the ED (59% vs. 37%; difference, 21.9 percentage points [-26.5 to -17.1 percentage points]). Compared with Delta, Omicron infection was independently associated with a lower risk for ICU admission (adjusted difference, 11.4 percentage points [CI, 8.4 to 14.4 percentage points]), mechanical ventilation (adjusted difference, 3.6 percentage points [CI, 1.7 to 5.6 percentage points]), and in-hospital mortality (adjusted difference, 4.2 percentage points [CI, 2.0 to 6.5 percentage points]).

LIMITATION

Patients with COVID-19 illness and no SARS-CoV-2 variant determination in the ED were excluded.

CONCLUSION

Compared with the Delta variant, infection with the Omicron variant in patients in the ED had different clinical and biological patterns and was associated with better in-hospital outcomes, including higher survival.

PRIMARY FUNDING SOURCE

None.

An assessment of the vaccination of school-aged children in England against SARS-CoV-2

BACKGROUND

Children and young persons are known to have a high number of close interactions, often within the school environment, which can facilitate rapid spread of infection; yet for SARS-CoV-2, it is the elderly and vulnerable that suffer the greatest health burden. Vaccination, initially targeting the elderly and vulnerable before later expanding to the entire adult population, has been transformative in the control of SARS-CoV-2 in England. However, early concerns over adverse events and the lower risk associated with infection in younger individuals means that the expansion of the vaccine programme to those under 18 years of age needs to be rigorously and quantitatively assessed.

METHODS

Here, using a bespoke mathematical model matched to case and hospital data for England, we consider the potential impact of vaccinating 12-17 and 5-11-year-olds. This analysis is reported from an early model (generated in June 2021) that formed part of the evidence base for the decisions in England, and a later model (from November 2021) that benefits from a richer understanding of vaccine efficacy, greater knowledge of the Delta variant wave and uses data on the rate of vaccine administration. For both models, we consider the population wide impact of childhood vaccination as well as the specific impact on the age groups targeted for vaccination.

RESULTS

Projections from June suggested that an expansion of the vaccine programme to those 12-17 years old could generate substantial reductions in infection, hospital admission and deaths in the entire population, depending on population behaviour following the relaxation of control measures. The benefits within the 12-17-year-old cohort were less marked, saving between 660 and 1100 (95% PI (prediction interval) 280-2300) hospital admissions and between 22 and 38 (95% PI 9-91) deaths depending on assumed population behaviour. For the more recent model, the benefits within this age group are reduced, saving on average 630 (95% PI 300-1300) hospital admissions and 11 (95% PI 5-28) deaths for 80% vaccine uptake, while the benefits to the wider population represent a reduction of 8-10% in hospital admissions and deaths. The vaccination of 5-11-year-olds is projected to have a far smaller impact, in part due to the later roll-out of vaccines to this age group.

CONCLUSIONS

Vaccination of 12-170-year-olds and 5-11-year-olds is projected to generate a reduction in infection, hospital admission and deaths for both the age groups involved and the population in general. For any decision involving childhood vaccination, these benefits needs to be balanced against potential adverse events from the vaccine, the operational constraints on delivery and the potential for diverting resources from other public health campaigns.

Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated a clear need for high-throughput, multiplexed and sensitive assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses and their emerging variants. Here, we present a cost-effective virus and variant detection platform, called microfluidic Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (mCARMEN), which combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel to test for up to 21 viruses, including SARS-CoV-2, other coronaviruses and both influenza strains, and demonstrated its diagnostic-grade performance on 525 patient specimens in an academic setting and 166 specimens in a clinical setting. We further developed an mCARMEN panel to enable the identification of 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 2,088 patient specimens with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of SARS-CoV-2 and influenza A viral copies in samples. The mCARMEN platform enables high-throughput surveillance of multiple viruses and variants simultaneously, enabling rapid detection of SARS-CoV-2 variants.

Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2 Omicron and Delta variants

Virus-like particle (VLP) and live virus assays were used to investigate neutralizing immunity against Delta and Omicron SARS-CoV-2 variants in 259 samples from 128 vaccinated individuals. Following Delta breakthrough infection, titers against WT rose 57-fold and 3.1-fold compared with uninfected boosted and unboosted individuals, respectively, versus only a 5.8-fold increase and 3.1-fold decrease for Omicron breakthrough infection. Among immunocompetent, unboosted patients, Delta breakthrough infections induced 10.8-fold higher titers against WT compared with Omicron (p = 0.037). Decreased antibody responses in Omicron breakthrough infections relative to Delta were potentially related to a higher proportion of asymptomatic or mild breakthrough infections (55.0% versus 28.6%, respectively), which exhibited 12.3-fold lower titers against WT compared with moderate to severe infections (p = 0.020). Following either Delta or Omicron breakthrough infection, limited variant-specific cross-neutralizing immunity was observed. These results suggest that Omicron breakthrough infections are less immunogenic than Delta, thus providing reduced protection against reinfection or infection from future variants.

Comparison of different sequencing techniques for identification of SARS-CoV-2 variants of concern with multiplex real-time PCR

As different SARS-CoV-2 variants emerge and with the continuous evolvement of sub lineages of the delta variant, it is crucial that all countries carry out sequencing of at least >1% of their infections, in order to detect emergence of variants with higher transmissibility and with ability to evade immunity. However, due to limited resources as many resource poor countries are unable to sequence adequate number of viruses, we compared to usefulness of a two-step commercially available multiplex real-time PCR assay to detect important single nucleotide polymorphisms (SNPs) associated with the variants and compared the sensitivity, accuracy and cost effectiveness of the Illumina sequencing platform and the Oxford Nanopore Technologies’ (ONT) platform. 138/143 (96.5%) identified as the alpha and 36/39 (92.3%) samples identified as the delta variants due to the presence of lineage defining SNPs by the multiplex real time PCR, were assigned to the same lineage by either of the two sequencing platforms. 34/37 of the samples sequenced by ONT had <5% ambiguous bases, while 21/37 samples sequenced using Illumina generated <5%. However, the mean PHRED scores averaged at 32.35 by Illumina reads but 10.78 in ONT. This difference results in a base error probability of 1 in 10 by the ONT and 1 in 1000 for Illumina sequencing platform. Sub-consensus single nucleotide variations (SNV) are highly correlated between both platforms (R² = 0.79) while indels appear to have a weaker correlation (R² = 0.13). Although the ONT had a slightly higher error rate compared to the Illumina technology, it achieved higher coverage with a lower number or reads, generated less ambiguous bases and was significantly less expensive than Illumina sequencing technology.