ABSTRACT
The recent coronavirus disease 2019 (COVID-19) outbreak revealed the susceptibility of elderly patients to respiratory virus infections, showing cell senescence or subclinical persistent inflammatory profiles and favouring the development of severe pneumonia. In our study, we evaluated the potential influence of lung aging on the efficiency of replication of influenza A virus (IAV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2), as well as determined the pro-inflammatory and antiviral responses of the distal lung tissue. Using precision-cut lung slices (PCLS) from donors of different ages, we found that pandemic H1N1 and avian H5N1 IAV replicated in the lung parenchyma with high efficacy. In contrast to these IAV strains, SARS-CoV-2 early isolate and Delta variant of concern (VOC) replicated less efficiently in PCLS. Interestingly, both viruses showed reduced replication in PCLS from older compared to younger donors, suggesting that aged lung tissue represents a sub optimal environment for viral replication. Regardless of the age-dependent viral loads, PCLS responded to infection with both viruses by an induction of IL-6 and IP-10/CXCL10 mRNAs, being highest for H5N1. Finally, while SARS-CoV-2 infection was not causing detectable cell death, IAV infection caused significant cytotoxicity and induced significant early interferon responses. In summary, our findings suggest that aged lung tissue might not favour viral dissemination, pointing to a determinant role of dysregulated immune mechanisms in the development of severe disease.
Subject(s)
Pneumonia , Severe Acute Respiratory Syndrome , Respiratory Tract Infections , Drug-Related Side Effects and Adverse Reactions , COVID-19 , Influenza, HumanABSTRACT
Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6,500 SARS-CoV-2 Alpha genomes (B.1.1.7) across seven months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are sub-clusters. Mobile service data can indicate these clusters' spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept proved successful as we introduced a mobility-guided sampling strategy for the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.
ABSTRACT
Background Understanding the clinical spectrum of SARS-CoV-2 infection, including the asymptomatic fraction, is important as asymptomatic individuals are still able to infect other individuals and contribute to ongoing transmission. The WHO Unity Household transmission investigation (HHTI) protocol provides a platform for the prospective and systematic collection of high-quality clinical, epidemiological, serological, and virological data from SARS-CoV-2 confirmed cases and their household contacts. These data can be used to understand key severity and transmissibility parameters - including the asymptomatic proportion - in relation to local epidemic context and help inform public health response. Methods We aimed to estimate the asymptomatic proportion of SARS-CoV-2 Omicron-variant infections in Unity-aligned HHTIs. We conducted a systematic review and meta-analysis in alignment with the PRISMA 2020 guidelines and registered our systematic review on PROSPERO (CRD42022378648). We searched EMBASE, Web of Science, MEDLINE, and bioRxiv and medRxiv from 1 November 2021 to 22 August 2023. Results We identified 8,368 records, of which 98 underwent full text review. We identified only three studies for data extraction, with substantial variation in study design and corresponding estimates of the asymptomatic proportion. As a result, we did not generate a pooled estimate or I2 metric. Conclusions The limited number of quality studies that we identified highlights the need for improved preparedness and response capabilities to facilitate robust HHTI implementation, analysis and reporting, to better inform national, regional and global risk assessments and policy making.
Subject(s)
COVID-19ABSTRACT
IntroductionDuring the COVID-19 pandemic, SARS-CoV-2 antigen rapid detection tests (RDTs) emerged as point-of-care diagnostics in addition to the RT-qPCR as the gold standard for SARS-CoV-2 diagnostics. Facing the course of the COVID-19 pandemic to an endemic characterised by several SARS-CoV-2 virus variants of concern (VOC) and an increasing public COVID-19 vaccination rate the aim of the study was to investigate the long-term test performance of SARS-CoV-2 RDT in large-scale, clinical screening use during and its influencing factors, above all SARS-CoV-2 VOC and COVID-19 vaccination. MethodsIn a prospective performance assessment conducted at a single centre tertiary care hospital, RDTs from three manufacturers (NADAL(R), Panbio, MEDsan(R)) were compared to RT-qPCR among individuals aged [≥] 6 month. The evaluation involved the determination of standardised viral load from oropharyngeal swabs as well as the evaluation of their influencing factors, especially the COVID-19 vaccination, for detecting SARS-CoV-2 in a clinical point-of-care environment spanning from 12 November 2020 to 30 June 2023 among patients, staff, and visitors of the hospital. ResultsAmong the 78,798 RDT/RT-qPCR tandems analysed, 2,016 (2.6%) tandems tested positive for SARS-CoV-2, with an overall sensitivity of 34.5% (95% CI 32.4-36.6%). A logistic regression revealed that typical COVID-19 symptoms significantly declined over the course of the study and throughout the COVID-19 pandemic, and that among the vaccinated, significantly fewer presented with an infection exhibiting typical symptoms. The employed lasso regression model indicated that only higher viral load and typical COVID-19 symptoms significantly increase the likelihood of a positive RDT result in the case of a SARS-CoV-2 infection directly. ConclusionOur findings indicate that only viral load and COVID-19 symptoms directly influence RDT performance while the obtained effects of COVID-19 vaccination and Omicron VOC both reducing RDT performance were mediated by these two factors. RDTs remain an adequate diagnostic tool for detecting SARS-CoV-2 in individuals showing respiratory symptoms. RDTs show promise beyond SARS-CoV-2, proving adaptable for detecting other pathogens like Influenza and RSV, highlighting their ongoing importance in infection control and prevention efforts.
Subject(s)
COVID-19 , Severe Acute Respiratory SyndromeABSTRACT
The COVID-19 pandemic has resulted in the loss of millions of lives, although a majority of those infected have managed to survive. Consequently, a set of outcomes, identified as long COVID, is now emerging. While the primary target of SARS-CoV-2 is the respiratory system, the impact of COVID-19 extends to various body parts, including the bone. This study aims to investigate the effects of acute SARS-CoV-2 infection on osteoclastogenesis, utilizing both ancestral and Omicron viral strains. Monocyte-derived macrophages (MDM), which serve as precursors to osteoclasts, were exposed to both viral variants. However, the infection proved abortive, even though ACE2 receptor expression increased post-infection, with no significant impact on cellular viability and redox balance. Both SARS-CoV-2 strains heightened osteoclast formation in a dose-dependent manner, as well as CD51/61 expression and bone resorptive ability. Notably, SARS-CoV-2 induced early pro-inflammatory M1 macrophage polarization, shifting towards an M2-like profile. Osteoclastogenesis-related genes (RANK, NFATc1, DC-STAMP, MMP9) were upregulated, and surprisingly, SARS-CoV-2 variants promoted RANKL-independent osteoclast formation. This thorough investigation illuminates the intricate interplay between SARS-CoV-2 and osteoclast precursors, suggesting potential implications for bone homeostasis and opening new avenues for therapeutic exploration in COVID-19.
Subject(s)
Severe Acute Respiratory Syndrome , Bone Diseases , COVID-19ABSTRACT
We mapped the 2020-2023 daily Covid-19 case data from the World Health Organization (WHO) to the original SIR model of Karmack and McKendrick for multiple pandemic recurrences due to the evolution of the virus to different variants in forty countries worldwide. The aim of the study was to determine how the SIR parameters are changing as the virus evolved into variants. Each peak in cases was analyzed separately for each country and the parameters: reff (pandemic R-parameter), Leff (average number of days an individual is infective) and (the rate of infection for contacts between the set of susceptible persons and the set of infected persons) were computed. Each peak was mapped to circulating variants for each country and the SIR parameters (reff, Leff, ) were averaged over each variant using their values in peaks where 70% of the variant sequences identified belonged to a single variant. This analysis showed that on average, compared to the original Wuhan variant ( = 0.2), the parameter has increased to = 0.5 for the Omicron variants. The value of reff has decreased from around 3.8 to 2.0 and Leff has decreased from 15 days to 10 days. This is as would be expected of a virus that is coming to equilibrium by evolving to increase its infectivity while reducing the effects of infections on the host.
Subject(s)
COVID-19ABSTRACT
Background/ObjectivesCOVID-19 continues to pose a significant burden that impacts public health and the healthcare system as the SARS-CoV-2 virus continues to evolve. Regularly updated vaccines are anticipated to boost waning immunity and provide protection against circulating variants. This study evaluated vaccine effectiveness (VE) of mRNA-1273.815, a 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine, at preventing COVID-19-related hospitalizations and any medically attended COVID-19 in adults [≥]18 years, overall, and by age and underlying medical conditions. MethodsThis retrospective cohort study used the Veradigm Network EHR linked to claims data to identify US adults [≥]18 years of age who received the mRNA-1273.815 vaccine (exposed) matched 1:1 to individuals who did not receive a 2023-2024 updated COVID-19 vaccine (unexposed). Patients in the unexposed cohort were randomly matched to eligible mRNA-1273.815 recipients. Inverse probability of treatment weighting was used to adjust for differences between the two cohorts. The exposed cohort was vaccinated between September 12, 2023, and December 15, 2023, and individuals in both cohorts were followed up for COVID-19-related hospitalizations and medically attended COVID-19 until December 31, 2023. A Cox regression model was used to estimate the hazard ratio (HR). VE of the mRNA-1273.815 vaccine in preventing COVID-19-related hospitalizations and any medically attended COVID-19 was estimated as 100*(1-HR). Subgroup analyses were performed for adults [≥]50, adults [≥]65, and individuals with underlying medical conditions associated with severe COVID-19 outcomes. ResultsOverall, 859,335 matched pairs of mRNA-1273.815 recipients and unexposed adults were identified. The mean age was 63 years, and 80% of the study population was [≥]50 years old. 61.5% of the mRNA-1273.815 cohort and 66.4% of the unexposed cohort had an underlying medical condition. Among the overall adult population ([≥]18 years), VE was 60.2% (53.4-66.0%) against COVID-19-related hospitalization and 33.1% (30.2%-35.9%) against medically attended COVID-19 over a median follow-up of 63 (IQR: 44-78) days. VE estimates by age and underlying medical conditions were similar. ConclusionsThese results demonstrate the significant protection provided by mRNA-1273.815 against COVID-19-related hospitalizations and any medically attended COVID-19 in adults 18 years and older, regardless of their vaccination history, and support CDC recommendations for vaccination with the 2023-2024 Omicron XBB.1.5-containing COVID-19 vaccine to prevent COVID-19-related outcomes, including hospitalizations.
Subject(s)
COVID-19ABSTRACT
The rapid emergence and global dissemination of SARS-CoV-2 highlighted a need for robust, adaptable surveillance systems. However, financial and infrastructure requirements for whole genome sequencing (WGS) mean most surveillance data have come from higher-resource geographies, despite unprecedented investment in sequencing in low-middle income countries (LMICs) throughout the SARS-CoV-2 pandemic. Consequently, the molecular epidemiology of SARS-CoV-2 in some LMICs is limited, and there is a need for more cost-accessible technologies to help close data gaps for surveillance of SARS-CoV-2 variants. To address this, we have developed two high-resolution melt curve (HRM) assays that target key variant-defining mutations in the SARS-CoV-2 genome, which give unique signature profiles that define different SARS-CoV-2 variants of concern (VOCs). Extracted RNA from SARS-CoV-2 positive samples collected from 205 participants (112 in Burkina Faso, 93 in Kenya) on the day of enrolment in the MALCOV study (Malaria as a Risk Factor for COVID-19) between February 2021 and February 2022 were analysed using our optimised HRM assays and compared to Next Generation Sequencing (NGS) on Oxford Nanopore MinION . With NGS as a reference, two HRM assays, HRM-VOC-1 and HRM-VOC-2, demonstrated sensitivity/specificity of 100%/99.29% and 92.86/99.39%, respectively, for detecting Alpha, 90.08%/100% and 92.31%/100% for Delta and 93.75%/100% and 100%/99.38% for Omicron. The assays described here provide a lower-cost approach (<$1 per sample) to conducting molecular epidemiology, capable of high-throughput testing. We successfully scaled up the HRM-VOC-2 assay to screen a total of 506 samples from which we were able to show the replacement of Alpha with the introduction of Delta and the replacement of Delta by the Omicron variant in this community in Kisumu, Kenya. These assays are readily adaptable and can focus on local epidemiological surveillance questions or be updated quickly to accommodate the emergence of a novel variant or adapt to novel and emerging pathogens.
Subject(s)
COVID-19 , Malaria , Genomic InstabilityABSTRACT
Currently, SARS-CoV-2 has evolved into various variants, including the numerous highly mutated Omicron sub-lineages, significantly increasing immune evasion ability. The development raises concerns about possibly diminished effectiveness of available vaccines and antibody-based therapeutics. Here, we describe those representative categories of broadly neutralizing antibodies (bnAbs) that retain prominent effectiveness against emerging variants including Omicron sub-lineages. The molecular characteristics, epitope conservation, and resistance mechanisms of these antibodies are further detailed, aiming to offer suggestion or direction for the development of therapeutic antibodies, and facilitate the vaccine design with broad-spectrum potential.
ABSTRACT
Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.
Subject(s)
COVID-19ABSTRACT
Objective: The study aimed to characterize the changing landscape of circulating SARS-CoV-2 lineages in the local community of Hong Kong throughout 2022. We examined how adjustments to quarantine arrangements influenced the transmission pattern of Omicron variants in a city with relatively rigorous social distancing measures at that time. Methods: In 2022, a total of 4,684 local SARS-CoV-2 genomes were sequenced using the Oxford Nanopore GridION sequencer. SARS-CoV-2 consensus genomes were generated by MAFFT, and the maximum likelihood phylogeny of these genomes were determined using IQ-TREE. The dynamic changes in lineages were depicted in a time tree created by Nextstrain. Statistical analysis was conducted to assess the correlation between changes in the number of lineages and adjustments to quarantine arrangements. Results: By the end of 2022, a total of 83 SARS-CoV-2 lineages were identified in the community. The increase in the number of new lineages was significantly associated with the relaxation of quarantine arrangements (One-way ANOVA, F(5,47)=18.233, p<0.001)). Over time, Omicron BA.5 sub-lineages replaced BA.2.2 and became the predominant Omicron variants in Hong Kong. The influx of new lineages reshaped the dynamics of Omicron variants in the community without fluctuating the death rate and hospitalization rate (One-way ANOVA, F(5,47)=2.037, p=0.091). Conclusion: The study revealed that even with an extended mandatory quarantine period for incoming travelers, it may not be feasible to completely prevent the introduction and subsequent community spread of highly contagious Omicron variants. Ongoing molecular surveillance of COVID-19 remains essential to monitor the emergence of new recombinant variants.
Subject(s)
COVID-19ABSTRACT
In this work the Luria and Delbruck Fluctuation Test was comparatively applied to the data of Morbidity by COVID-19 in the United States of America (USA), United Kingdom (UK), Taiwan and China from 2020 to 2023. Three types of data were used: es.statista.com, datosmacro.expansion.com and larepublica.co without modification, but trying to avoid and justify the anomalies and inconsistencies observed. The methods originally used to establish the interactions of two populations were evaluated: the viral population with that of its host and the drift of both organisms. Only the interactive fluctuations of the weekly Variance of daily increase of Cases (Morbidity) were studied. The results showed that the Fluctuation Test is applicable to the selected data from USA, UK, Taiwan and China and other data from several countries used as controls. The study was separated into two approaches: First, comparison of the total or partial logarithmic profile of fluctuations of Variance of Cases (Morbidity) of USA, UK, Taiwan and China. Second, comparison of the values of the first fluctuation of Variance of Cases (Morbidity) in the boreal winter of 2020 for USA, UK, Taiwan, China and several countries used as controls. The results obtained for Morbidity demonstrate that USA and UK present a similar bimodal profile. China shows an inverted profile and Taiwan shows an intermediate profile between both tendencies. However, it was possible to detect some anomalies and uncertainties that were possibly derived from inconsistencies in the original data. Only USA shows a value of the first fluctuation comparable to the order of magnitude of the value of the first fluctuation of the Variance of Cases of China, in the northern winter of 2020. In the First Approach USA, UK and China had two important fluctuations: the first in the northern winter of 2020 before week 16 and the second at the beginning of northern winter of 2022, more than 100 weeks later. Taiwan showed only the latter. This latest fluctuation coincides with two events: the possible achievement of herd immunity and the emergence of Omicron variant. In this work we have evaluated whether this coincidence is casual or causal. The results obtained in the Second Approach aim to confirm the hypothesis of the animal origin of the first variant of SARS CoV-2.
Subject(s)
COVID-19 , Abnormalities, Drug-InducedABSTRACT
Boosting with mRNA vaccines encoding variant-matched spike proteins has been implemented to mitigate their reduced efficacy against emerging SARS-CoV-2 variants. Nonetheless, in humans, it remains unclear whether boosting in the ipsilateral or contralateral arm with respect to the priming doses impacts immunity and protection. Here, we boosted K18-hACE2 mice with either monovalent mRNA-1273 (Wuhan-1 spike) or bivalent mRNA-1273.214 (Wuhan-1 + BA.1 spike) vaccine in the ipsilateral or contralateral leg relative to a two-dose priming series with mRNA-1273. Boosting in the ipsilateral or contralateral leg elicited equivalent levels of serum IgG and neutralizing antibody responses against Wuhan-1 and BA.1. While contralateral boosting with mRNA vaccines resulted in expansion of spike-specific B and T cells beyond the ipsilateral draining lymph node (DLN) to the contralateral DLN, administration of a third mRNA vaccine dose at either site resulted in similar levels of antigen-specific germinal center B cells, plasmablasts/plasma cells, T follicular helper cells and CD8+ T cells in the DLNs and the spleen. Furthermore, ipsilateral and contralateral boosting with mRNA-1273 or mRNA-1273.214 vaccines conferred similar homologous or heterologous immune protection against SARS-CoV-2 BA.1 virus challenge with equivalent reductions in viral RNA and infectious virus in the nasal turbinates and lungs. Collectively, our data show limited differences in B and T cell immune responses after ipsilateral and contralateral site boosting by mRNA vaccines that do not substantively impact protection against an Omicron strain.
ABSTRACT
BackgroundSARS-CoV-2 infection elicits distinct clinical features in children and adults. Profiling the adaptive immune response following infection in children is essential to better understand and characterize these differences. MethodsHumoral and cell-mediated immune responses from unvaccinated pediatric and adult participants were analyzed following asymptomatic or mild non-Omicron SARS-CoV-2 infection. Levels of IgG and IgA targeting spike (S), receptor-binding domain (RBD), and nucleocapsid (N) proteins of SARS-CoV-2 were measured, while neutralizing antibody (nAb) titers were assessed against three viral strains (Wuhan, Omicron BA.1 and BA.4/BA.5). Specific T-cell memory responses were investigated by quantifying interferon-gamma (IFN-{gamma}) secreting cells after stimulation with ancestral and variant strains of SARS-CoV-2, and seasonal human {beta}- coronaviruses (HCoV)-OC43 and -HKU1. ResultsThe study comprised 28 children (3 to 17 [median=10] years old) and 28 adults (19 to 62 [median=42]). At a mean time of seven months ({+/-} 2.8 months) after SARS-CoV-2 infection, children and adults mounted comparable antibody levels against S and RBD, as well as similar neutralization capacity. However, children displayed a weaker cellular memory response to SARS- CoV-2 than adults, with a median of 88 [28-184] spot forming units per million of PBMCs in children compared to 208 [141-340] in adults (***, P < .001). In children, the level of IFN-{gamma} secreting cells in response to SARS-CoV-2 corresponds to that of seasonal coronaviruses. ConclusionLong-term memory T-cell responses to SARS-CoV-2 are enhanced in adults compared to children who demonstrate equivalent responses to SARS-CoV-2 and other HCoV. HIGHLIGHTSO_LIChildren infected with SARS-CoV-2 show comparable binding and neutralizing antibody levels as adults seven months after infection. C_LIO_LIThere are notable differences in the intensity of the T-cell response following SARS-CoV- 2 infection between children and adults. C_LIO_LIChildren have more pronounced T-cell immunodominance towards the spike versus non- spike proteins compared to adults at seven months post-infection C_LIO_LIIn contrast, T-cell responses to SARS-CoV-2 are globally reduced in children compared to adults but are alike to other seasonal {beta}-coronaviruses. C_LI
Subject(s)
COVID-19ABSTRACT
Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with five to six-fold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a five-fold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.
Subject(s)
Infections , Severe Acute Respiratory Syndrome , COVID-19ABSTRACT
Background: SARS-CoV-2 vaccines have been shown to be safe and effective against infection and severe COVID-19 disease worldwide. Certain co-morbid conditions cause immune dysfunction and may reduce immune response to vaccination. In contrast, those with co-morbidities may practice infection prevention strategies. Thus, the real-world clinical impact of co-morbidities on SARS-CoV-2 infection in the recent post-vaccination period is not well established. We performed this study to understand the epidemiology of Omicron breakthrough infection and evaluate associations with number of comorbidities in a vaccinated and boosted population. Methods and Findings: We performed a retrospective clinical cohort study utilizing the Northwestern Medicine Enterprise Data Warehouse. Our study population was identified as fully vaccinated adults with at least one booster. The primary risk factor of interest was the number of co-morbidities. Our primary outcome was incidence and time to first positive SARS-CoV-2 molecular test in the Omicron predominant era. We performed multivariable analyses stratified by calendar time using Cox modeling to determine hazard of SARS-CoV-2. In total, 133,191 patients were analyzed. Having 3+ comorbidities was associated with increased hazard for breakthrough (HR=1.2 CI 1.2-1.6). During the second half of the study, having 2 comorbidities (HR= 1.1 95% CI 1.02-1.2) and having 3+ comorbidities (HR 1.7, 95% CI 1.5-1.9) were associated with increased hazard for Omicron breakthrough. Older age was associated with decreased hazard in the first 6 months of follow-up. Interaction terms for calendar time indicated significant changes in hazard for many factors between the first and second halves of the follow-up period. Conclusions: Omicron breakthrough is common with significantly higher risk for our most vulnerable patients with multiple co-morbidities. Age related behavioral factors play an important role in breakthrough infection with the highest incidence among young adults. Our findings reflect real-world differences in immunity and exposure risk behaviors for populations vulnerable to COVID-19.
Subject(s)
Breakthrough Pain , Immune System Diseases , COVID-19ABSTRACT
When fitting a multi-parameter model to a data set, computer algorithms may suggest that a range of parameters provide equally reasonable fits, making the parameter estimation difficult. Here, we prove this fact for an SIR model. We say a set of parameter values is a good fit to outbreak data if the solution has the data's three most significant characteristics: the standard deviation, the mean time, and the total number of cases. In our model, in addition to the "basic reproduction number" $R_0$, three other parameters need to be estimated to fit a solution to outbreak data. We will show that those parameters can be chosen so that each gives a linear transformation of a solution's incidence data. As a result, we show that for every choice of $R_0>1$, there is a good fit for each outbreak. We also illustrate our results by providing the least square best fits of the New York City and London data sets of the Omicron variant of COVID-19. Furthermore, we show how versions of the SIR model with $N$ compartments have far more good fits- - indeed a high dimensional set of good fits -- for each target -- showing that more complicated models may have an even greater problem in overparametrizing outbreak characteristics.
Subject(s)
COVID-19ABSTRACT
ObjectivesTo inform management of competing risks from Covid-19 and key-worker absence, we evaluated whether using two manufacturers lateral flow tests (LFTs) concurrently improved SARS-CoV-2 Omicron detection and was acceptable to hospital staff. In a nested study, to understand the risks of return to work after a fixed number of days of isolation or quarantine, we examined virus culture at Days 5-7 after positive test or significant exposure. Methods and Analysis1419 fully-vaccinated Liverpool (UK) University Hospitals staff participated in a random-order, open-label trial testing whether dual LFTs improved SARS-CoV2 detection, and whether dual swabbing was acceptable to users. Main outcome was self-reported LFT result. Staff enrolled via routine testing sites for symptomatic staff and close contacts. Recruitment took place between 7th February and 8th May 2022. Participants employed nose-throat swab Innova and nose-only swab Orient Gene LFTs for 10 days, with daily LFTs taken in random order. A swab for polymerase chain reaction (PCR) analysis was taken at Day-5 and, if positive, Day-10. A questionnaire on acceptability was administered on exit. Selected participants gave swabs for viral culture on Days 5-7; swabs were delivered and returned by courier. Cultures were considered positive if cytopathic effect was apparent or the SARs-COV2 N gene sub-genomic RNA was detected by sequencing. Results226 individuals reported 1466 pairs of LFT results. Tests disagreed in 127 cases (8.7%). Orient Gene was more likely (78 cf. 49, P=0.03) to be positive. Orient Gene positive Innova negative result-pairs became more frequent over time (P<0.001). If Innova was swabbed second, it was less likely to agree with a positive Orient Gene result (P=0.005); swabbing first with Innova made no significant difference (P=0.85). Of 311 individuals completing the exit questionnaire, 90.7% reported dual swabbing was easy, 57.1% said it was no barrier to their daily routine and 65.6% preferred dual testing. Respondents had more confidence in dual c.f. single test results (median 9 cf. 8 on 10-point scale, P<0.001). Viral cultures from swabs taken at Days 5-7 were positive for 6/31 (19.4%, 7.5%-37.5%) and indeterminate for 11/31 (35.5%, 19.2%-54.6%) LFT-positive participants, indicating they were likely still infectious. ConclusionsDual brand testing increased LFT detection of SARS-CoV-2 antigen by a small but meaningful margin and was acceptable to hospital workers. Viral cultures demonstrated that policies recommending safe return to work [~]5 days after Omicron infection/exposure were flawed. Key-workers should be prepared for dynamic self-testing protocols in future pandemics. Trial registrationhttps://www.isrctn.com/ISRCTN47058442 (IRAS Project ID:311842) Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIOmicron BA.1 and BA.2 waves caused large-scale healthcare worker absence in late 2021 - early 2022, risking patient safety from both Covid-19 and reduced care capacity C_LIO_LILateral flow tests (LFTs) reliably detected SARS-CoV-2 antigen, more so with Omicron than prior variants, identifying the most infectious individuals C_LIO_LISelf-testing with LFT SARS-CoV-2 rapid antigen tests reduced Covid-19 transmission, mitigating risks of return to work, including healthcare settings C_LI What this study addsO_LIDual c.f. single brand LFT testing increased SARS-CoV-2 antigen detection marginally, but more than can be explained by extending swabbing from nose-only to nose-throat C_LIO_LINHS deployment of nose-only LFTs in response to compound pressures from Omicron, winter and pandemic burnout was safe and acceptable to most participating hospital staff C_LIO_LICulturable virus was detected confidently in a fifth (and potentially in a further third) of LFT-positive hospital workers 5-7 days after their self-referral for testing, indicating substantial protracted infectiousness C_LI How this study might affect research, practice or policyO_LIThis study shows international Covid-19 policies for return to work after fixed periods (e.g. 5 days after positive test) were flawed: too little emphasis was placed on variation in infectivity between individuals C_LIO_LIFuture pandemic preparedness needs to plan testing quality assurance unified across healthcare and community self-testing contexts, including continuous study of serial daily antigen, nucleic acid and culturable virus test results C_LI
Subject(s)
COVID-19ABSTRACT
BackgroundAccurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow, as severe patients require intensive care. Current techniques lack the ability to accurately predict COVID-19 patients clinical type, especially as SARS-CoV-2 continues to mutate. ObjectiveIn this work, we explore both predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and COVID-19 variants. MethodsComprehensive patient-level data were collected from 362 patients (214 severe, 148 non-severe) with the original SARS-CoV-2 variant in 2020 and 1000 patients (500 severe, 500 non-severe) with the Omicron variant in 2022-2023. The data included 26 biochemical features from blood testing and 26 clinical features from each patients clinical characteristics and medical history. Different types of ML techniques, including penalized logistic regression (LR), random forest (RF), k-nearest neighbors (kNN), and support vector machines (SVM) were applied to build predictive models based on each data modality separately and together for each variant set. ResultsAll ML models performed similarly under different testing scenarios. The fused characteristic modality yielded the highest area under the curve (AUC) score achieving 0.914 on average. The second highest AUC was 0.876 achieved by the biochemical modality alone, followed by 0.825 achieved by clinical modality alone. All ML models were robust when cross-tested with original and Omicron variant patient data. Upon model interpretation, our models ranked elevated d-dimer (biochemical feature), elevated high sensitivity troponin I (biochemical feature), and age greater than 55 years (clinical feature) as the most predictive features of severe COVID-19. ConclusionsWe found ML to be a powerful tool for predicting severe COVID-19 based on comprehensive individual patient-level data. Further, ML models trained on the biochemical and clinical modalities together witness enhanced predictive power. The improved performance of these ML models when trained and cross-tested with Omicron variant data supports the robustness of ML as a tool for clinical decision support.