Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk

Optimising nucleic acid recovery from rapid antigen tests for whole genome sequencing of respiratory viruses

BACKGROUND

Whole genome sequencing (WGS) of respiratory viruses from rapid antigen tests (RAT-WGS) is a novel approach to expanding genomic surveillance of respiratory infections. To date however, there are limited data on the genomic stability of these viruses on RATs. In this study, we investigated the effect of storage conditions and nucleic acid preservatives on the ability to enhance stability and improve recovery of respiratory virus genomes from RATs.

METHODS

A mixture of common respiratory viruses was used to inoculate RATs at different environmental temperatures (4°C, 20°C and 36°C), with two preservative reagents (RNALater and DNA/RNA shield) Nucleic acid was extracted from RATs at two different timepoints (72 h and seven days) and subject to real-time multiplex respiratory PCR to detect a range of respiratory viruses. WGS was performed using target-enrichment with the TWIST Comprehensive Viral Research Panel. Defined metrics from an automated in-house bioinformatic pipeline were used to assess and compare viral genome recovery under different conditions.

RESULTS

Nucleic acid degradation (indicated by relative change in PCR cycle threshold and WGS-based metrics) was most notable at 20 °C and 36 °C. Storage in either RNALater or DNA / RNA shield improved genome recovery for respiratory viruses across all temperature conditions, although this was most pronounced for RNALater. Subtyping of Influenza viruses demonstrated the applicability of RAT-WGS in downstream genomic epidemiological surveillance.

CONCLUSIONS

Under simulated conditions, RAT-WGS demonstrated that (i) viral genomes were generally stable at 4°C at 72 h and 1 week, (ii) RNALater has a more significant preservation of nucleic acids compared to DNA/RNA Shield and (iii) genome recovery can be achieved using a sequencing depth of 500,000 reads per sample in RNALater, across all respiratory viruses and conditions.

Proposed new definition for hospital-acquired SARS-CoV-2 infections: results of a confirmatory factor analysis

Objective

The present study aims to develop and discuss an extension of hospital-acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections (HA-SIs) definition which goes beyond the use of time parameters alone.

Design

A confirmatory factor analysis was carried out to test a suitable definition for HA-SI.

Setting and Patients

A two-center cohort study was carried out at two tertiary public hospitals in the German state of lower Saxony. The study involved a population of 366 laboratory-confirmed SARS-CoV-2-infected inpatients enrolled between March 2020 and August 2023.

Results

The proposed model shows adequate fit indices (CFI.scaled = 0.959, RMSEA = 0.049). A descriptive comparison with existing classifications revealed strong features of our model, particularly its adaptability to specific regional outbreaks.

Conclusion

The use of the regional incidence as a proxy variable to better define HA-SI cases represents a pragmatic and novel approach. The model aligns well with the latest scientific results in the literature. This work successfully unifies, within a single model, variables which the recent literature described as significant for the onset of HA-SI. Further potential improvements and adaptations of the model and its applications, such as automating the categorization process (in terms of hospital acquisition) or employing a comparable model for hospital-acquired influenza classification, are subjects open for discussion.

SARS-CoV-2 rapid antigen testing positive rate in community testing stations as an indicator for COVID-19 epidemic trend, Taipei, Taiwan, May to August 2021

BACKGROUND

Real-time surveillance of COVID-19 in large-scale community outbreaks presents challenges. Simple counts of the daily confirmed cases can be misleading due to constraints from bottlenecks in access to care or laboratory testing. This study aimed to investigate the role of the SARS-CoV-2 antigen rapid diagnostic test (Ag-RDT) in addressing these challenges for real-time COVID-19 surveillance.

METHODS

This study included the results of 86,933 SARS-CoV-2 Ag-RDT and real-time reverse transcription polymerase chain reaction (RT-PCR) tests. These were conducted at four community testing stations within the Taipei metropolitan area during a community COVID-19 outbreak spanning from May 17, 2021, to August 9, 2021. We examined the correlation between the positive rates of Ag-RDT tests and the epidemic curve of laboratory-confirmed COVID-19 cases by onset date to examine its role in real-time surveillance.

RESULTS

During the 85-day study period, the trend of Ag-RDT test positive rates paralleled that of the epidemic curve. The correlation between the Ag-RDT positive rate and the number of cases (Pearson correlation coefficient: 0.968) is comparable to that of the RT-PCR positive rate (Pearson correlation coefficient: 0.964). The Ag-RDT positive rate exhibited a more advanced leading trend, with Ag-RDT leading by 3 days in comparison to the 2-day lead for RT-PCR.

CONCLUSION

The positive rate of SARS-CoV-2 Ag-RDT tests at community testing stations serves as a good surrogate for assessing virus activity within the community and a useful tool for real-time COVID-19 surveillance. It is a robust indicator of the outbreak trend and near-term numbers of cases. This finding may facilitate the management of subsequent outbreaks of emerging infectious diseases.

Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital

INTRODUCTION

Infection control measures are effective for nosocomial COVID-19 prevention but bear substantial health-economic costs, motivating their "de-escalation" in settings at low risk of SARS-CoV-2 transmission. Yet consequences of de-escalation are difficult to predict, particularly in light of novel variants and heterogeneous population immunity.

AIM

To estimate how infection control measure de-escalation influences nosocomial COVID-19 risk.

METHODS

An individual-based transmission model was used to simulate SARS-CoV-2 outbreaks and control measure de-escalation in a French long-term care hospital with multi-modal control measures in place (testing and isolation, universal masking, single-occupant rooms). Estimates of COVID-19 case fatality rates (CFRs) from reported outbreaks were used to quantify excess COVID-19 mortality due to de-escalation.

RESULTS

In a population fully susceptible to infection, de-escalating both universal masking and single rooms resulted in hospital-wide outbreaks of 114 (95% CI: 103-125) excess infections, compared with five (three to seven) excess infections when de-escalating only universal masking or 15 (11-18) when de-escalating only single rooms. When de-escalating both measures and applying CFRs from the first wave of COVID-19, excess patient mortality ranged from 1.57 (1.41-1.71) to 9.66 (8.73-10.57) excess deaths/1000 patient-days. By contrast, when applying CFRs from subsequent pandemic waves and assuming susceptibility to infection among 40-60% of individuals, excess mortality ranged from 0 (0-0) to 0.92 (0.77-1.07) excess deaths/1000 patient-days.

CONCLUSIONS

The de-escalation of bundled COVID-19 control measures may facilitate widespread nosocomial SARS-CoV-2 transmission. However, excess mortality is probably limited in populations at least moderately immune to infection and given CFRs resembling those estimated during the 'post-vaccine' era.

Rapid deep learning-assisted predictive diagnostics for point-of-care testing

Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.

Revolutionizing SARS-CoV-2 omicron variant detection: Towards faster and more reliable methods

The emergence of the highly contagious Omicron variant of SARS-CoV-2 has inflicted significant damage during the ongoing COVID-19 pandemic. This new variant's significant sequence changes and mutations in both proteins and RNA have rendered many existing rapid detection methods ineffective in identifying it accurately. As the world races to control the spread of the virus, researchers are urgently exploring new diagnostic strategies to specifically detect Omicron variants with high accuracy and sensitivity. In response to this challenge, we have compiled a comprehensive overview of the latest reported rapid detection techniques. These techniques include strategies for the simultaneous detection of multiple SARS-CoV-2 variants and methods for selectively distinguishing Omicron variants. By categorizing these diagnostic techniques based on their targets, which encompass protein antigens and nucleic acids, we aim to offer a comprehensive understanding of the utilization of various recognition elements in identifying these targets. We also highlight the advantages and limitations of each approach. Our work is crucial in providing a more nuanced understanding of the challenges and opportunities in detecting Omicron variants and emerging variants.

How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review

Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. The objective of this study was to systematically review SARS-CoV-2 transmission models in healthcare settings, and to summarize their contributions to understanding nosocomial COVID-19. A systematic search and review of published articles indexed in PubMed was carried out. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022 were included. Models have mostly focused on acute-care and long-term-care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.

Collateral impacts of pandemic COVID-19 drive the nosocomial spread of antibiotic resistance: A modelling study

BACKGROUND

Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. We sought to evaluate how such collateral impacts of COVID-19 impacted the nosocomial spread of MRB in an early pandemic context.

METHODS AND FINDINGS

We developed a mathematical model in which Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and MRB cocirculate among patients and staff in a theoretical hospital population. Responses to COVID-19 were captured mechanistically via a range of parameters that reflect impacts of SARS-CoV-2 outbreaks on factors relevant for pathogen transmission. COVID-19 responses include both "policy responses" willingly enacted to limit SARS-CoV-2 transmission (e.g., universal masking, patient lockdown, and reinforced hand hygiene) and "caseload responses" unwillingly resulting from surges in COVID-19 caseloads (e.g., abandonment of antibiotic stewardship, disorganization of infection control programmes, and extended length of stay for COVID-19 patients). We conducted 2 main sets of model simulations, in which we quantified impacts of SARS-CoV-2 outbreaks on MRB colonization incidence and antibiotic resistance rates (the share of colonization due to antibiotic-resistant versus antibiotic-sensitive strains). The first set of simulations represents diverse MRB and nosocomial environments, accounting for high levels of heterogeneity across bacterial parameters (e.g., rates of transmission, antibiotic sensitivity, and colonization prevalence among newly admitted patients) and hospital parameters (e.g., rates of interindividual contact, antibiotic exposure, and patient admission/discharge). On average, COVID-19 control policies coincided with MRB prevention, including 28.2% [95% uncertainty interval: 2.5%, 60.2%] fewer incident cases of patient MRB colonization. Conversely, surges in COVID-19 caseloads favoured MRB transmission, resulting in a 13.8% [-3.5%, 77.0%] increase in colonization incidence and a 10.4% [0.2%, 46.9%] increase in antibiotic resistance rates in the absence of concomitant COVID-19 control policies. When COVID-19 policy responses and caseload responses were combined, MRB colonization incidence decreased by 24.2% [-7.8%, 59.3%], while resistance rates increased by 2.9% [-5.4%, 23.2%]. Impacts of COVID-19 responses varied across patients and staff and their respective routes of pathogen acquisition. The second set of simulations was tailored to specific hospital wards and nosocomial bacteria (methicillin-resistant Staphylococcus aureus, extended-spectrum beta-lactamase producing Escherichia coli). Consequences of nosocomial SARS-CoV-2 outbreaks were found to be highly context specific, with impacts depending on the specific ward and bacteria evaluated. In particular, SARS-CoV-2 outbreaks significantly impacted patient MRB colonization only in settings with high underlying risk of bacterial transmission. Yet across settings and species, antibiotic resistance burden was reduced in facilities with timelier implementation of effective COVID-19 control policies.

CONCLUSIONS

Our model suggests that surges in nosocomial SARS-CoV-2 transmission generate selection for the spread of antibiotic-resistant bacteria. Timely implementation of efficient COVID-19 control measures thus has 2-fold benefits, preventing the transmission of both SARS-CoV-2 and MRB, and highlighting antibiotic resistance control as a collateral benefit of pandemic preparedness.

Is there a role for RDTs as we live with COVID-19? An assessment of different strategies

INTRODUCTION

By 2022, high levels of past COVID-19 infections, combined with substantial levels of vaccination and the development of Omicron, have shifted country strategies towards burden reduction policies. SARS-CoV-2 rapid antigen tests (rapid diagnostic tests (RDTs)) could contribute to these policies by helping rapidly detect, isolate and/or treat infections in different settings. However, the evidence to inform RDT policy choices in low and middle-income countries (LMICs) is limited.

METHOD

We provide an overview of the potential impact of several RDT use cases (surveillance; testing, tracing and isolation without and with surveillance; hospital-based screening to reduce nosocomial COVID-19; and testing to enable earlier/expanded treatment) for a range of country settings. We use conceptual models and literature review to identify which use cases are likely to bring benefits and how these may change with outbreak characteristics. Impacts are measured through multiple outcomes related to gaining time, reducing the burden on the health system and reducing deaths.

RESULTS

In an optimal scenario in terms of resources and capacity and with baseline parameters, we find marginal time gains of 4 days or more through surveillance and testing tracing and isolation with surveillance, a reduction in peak intensive care unit (ICU) or ICU admissions by 5% or more (hospital-based screening; testing, tracing and isolation) and reductions in COVID-19 deaths by over 6% (hospital-based screening; test and treat). Time gains may be used to strengthen ICU capacity and/or boost vulnerable individuals, though only a small minority of at-risk individuals could be reached in the time available. The impact of RDTs declines with lower country resources and capacity, more transmissible or immune-escaping variants and reduced test sensitivity.

CONCLUSION

RDTs alone are unlikely to dramatically reduce the burden of COVID-19 in LMICs, though they may have an important role alongside other interventions such as vaccination, therapeutic drugs, improved healthcare capacity and non-pharmaceutical measures.

Platinum-Decorated Gold Nanoparticle-Based Microfluidic Chip Immunoassay for Ultrasensitive Colorimetric Detection of SARS-CoV-2 Nucleocapsid Protein

Gold nanoparticle-based point-of-care tests (POCT) are one of the most widely used diagnostic tools for SARS-CoV-2 screening. However, the limitation of their insufficient sensitivity often leads to false negative results in early disease diagnostics. The ongoing pandemic of COVID-19 makes diagnostic tools that are more accurate, sensitive, simple, and affordable in high demand. In this work, we develop a platinum-decorated gold nanoparticle (Au@Pt NP)-based microfluidic chip immunoassay with a sensitivity surpassing that of paper-based detection of nucleocapsid (N) protein, one of the most conserved biomarkers of COVID-19. The synthesized Au@Pt NPs show high stability and catalytic activity in complex environments. The catalytic amplification of Au@Pt NPs enables naked-eye detection of N protein in the low femtogram range (ca. 0.1 pg/mL) and the detection of throat swab samples in under 40 min. This microfluidic chip immunoassay is easy for operation and readout without instrument assistance, making it more suitable for on-site detection and future pathogen surveillance.

Dual-mode SERS-based lateral flow assay strips for simultaneous diagnosis of SARS-CoV-2 and influenza a virus

Since COVID-19 and flu have similar symptoms, they are difficult to distinguish without an accurate diagnosis. Therefore, it is critical to quickly and accurately determine which virus was infected and take appropriate treatments when a person has an infection. This study developed a dual-mode surface-enhanced Raman scattering (SERS)-based LFA strip that can diagnose SARS-CoV-2 and influenza A virus with high accuracy to reduce the false-negative problem of the commercial colorimetric LFA strip. Furthermore, using a single strip, it is feasible to detect SARS-CoV-2 and influenza A virus simultaneously. A clinical test was performed on 39 patient samples (28 SARS-CoV-2 positives, 6 influenza A virus positives, and 5 negatives), evaluating the clinical efficacy of the proposed dual-mode SERS-LFA strip. Our assay results for clinical samples show that the dual-mode LFA strip significantly reduced the false-negative rate for both SARS-CoV-2 and influenza A virus.

Projections of the transmission of the Omicron variant for Toronto, Ontario, and Canada using surveillance data following recent changes in testing policies

At the end of 2021, with the rapid escalation of COVID19 cases due to the Omicron variant, testing centers in Canada were overwhelmed. To alleviate the pressure on the PCR testing capacity, many provinces implemented new strategies that promote self testing and adjust the eligibility for PCR tests, making the count of new cases underreported. We designed a novel compartmental model which captures the new testing guidelines, social behaviours, booster vaccines campaign and features of the newest variant Omicron. To better describe the testing eligibility, we considered the population divided into high risk and non-high-risk settings. The model is calibrated using data from January 1 to February 9, 2022, on cases and severe outcomes in Canada, the province of Ontario and City of Toronto. We conduct analyses on the impact of PCR testing capacity, self testing, different levels of reopening and vaccination coverage on cases and severe outcomes. Our results show that the total number of cases in Canada, Ontario and Toronto are 2.34 (95%CI: 1.22-3.38), 2.20 (95%CI: 1.15-3.72), and 1.97(95%CI: 1.13-3.41), times larger than reported cases, respectively. The current testing strategy is efficient if partial restrictions, such as limited capacity in public spaces, are implemented. Allowing more people to have access to PCR reduces the daily cases and severe outcomes; however, if PCR test capacity is insufficient, then it is important to promote self testing. Also, we found that reopening to a pre-pandemic level will lead to a resurgence of the infections, peaking in late March or April 2022. Vaccination and adherence to isolation protocols are important supports to the testing policies to mitigate any possible spread of the virus.

False-positivity results in rapid antigen tests for SARS-CoV-2: an umbrella review of meta-analyses and systematic reviews

INTRODUCTION

The rapid antigen detection tests (RADTs) for SARS-CoV-2 infection could contribute to the clinical and public health strategies for managing COVID-19. This umbrella review aimed to explore the accuracy and sensitivity of RADTs for SARS-CoV-2 by assessing the incidence of false positivity associated with them.

AREAS COVERED

Meta-analyses and systematic reviews on the sensitivity and specificity of commercially available RADTs with data on false-positive results were identified by searching the PubMed, EMBASE, Cochrane Library, and Web of Science databases from inception to March 31, 2022. All meta-analyses and systematic reviews on the sensitivity and specificity of rapid antigen tests were included. Data on the author and year, included studies, index tests, sample size, false negatives, false positives, and study quality based on AMSTAR 2 (Assessing the Methodological Quality of Systematic Reviews) rating were extracted from the included meta-analyses and systematic reviews.

EXPERT OPINION

We identified 12 meta-analyses and systematic review that presented data on the false-positive results in RADTs. The false positivity rates in the included studies ranged from 0.0% - 4.0%. This study summarizes the available evidence on the incidence of false positivity in RADTs and shows it is less than 4.0%. Therefore, our findings imply that RADTs can be an appropriate, economic, and rapid detection method for mass screening of COVID-19.