Limited Testing Capacity Research Articles

Underreporting of Cases in the COVID-19 Outbreak of Borriana (Spain) during Mass Gathering Events in March 2020: A Cross-Sectional Study.

Determining the number of cases of an epidemic is the first function of epidemiological surveillance. An important underreporting of cases was observed in many locations during the first wave of the COVID-19 pandemic. To estimate this underreporting in the COVID-19 outbreak of Borriana (Valencia Community, Spain) in March 2020, a cross-sectional study was performed in June 2020 querying the public health register. Logistic regression models were used. Of a total of 468 symptomatic COVID-19 cases diagnosed in the outbreak through anti-SARS-CoV-2 serology, 36 cases were reported (7.7%), resulting in an underreporting proportion of 92.3% (95% confidence interval [CI], 89.5-94.6%), with 13 unreported cases for every reported case. Only positive SARS-CoV-2 polymerase chain reaction cases were predominantly reported due to a limited testing capacity and following a national protocol. Significant factors associated with underreporting included no medical assistance for COVID-19 disease, with an adjusted odds ratio [aOR] of 10.83 (95% CI 2.49-47.11); no chronic illness, aOR = 2.81 (95% CI 1.28-6.17); middle and lower social classes, aOR = 3.12 (95% CI 1.42-6.85); younger age, aOR = 0.97 (95% CI 0.94-0.99); and a shorter duration of illness, aOR = 0.98 (95% CI 0.97-0.99). To improve the surveillance of future epidemics, new approaches are recommended.

High SARS-CoV-2 Exposure in Rural Southern Mozambique After Four Waves of COVID-19: Community-Based Seroepidemiological Surveys.

Mozambique was one of many African countries with limited testing capacity for SARS-CoV-2. Serosurveys, an alternative to estimate the real exposure to understand the epidemiology and transmission dynamics, have been scarce in Mozambique. Herein, we aimed to estimate the age-specific seroprevalence of SARS-CoV-2 in the general population of the Manhiça District, at four time points, for evaluating dynamics of exposure and the impact of vaccination. We conducted four community-based seroepidemiological surveys separated by 3 months between May 2021 and June 2022 to assess the prevalence of SARS-CoV-2 antibodies. An age-stratified (0-19, 20-39, 40-59, and ≥ 60 years) sample of 4810 individuals was randomly selected from demographic surveillance database, and their blood samples were analyzed using WANTAI SARS-CoV-2 IgG + IgM ELISA. Nasopharyngeal swabs from a subsample of 2209 participants were also assessed for active infection by RT-qPCR. SARS-CoV-2 seroprevalence increased from 27.6% in the first survey (May 2021) to 63.6%, 91.2%, and 91.1% in the second (October 2021), third (January 2022), and fourth (May 2022) surveys, respectively. Seroprevalence in individuals < 18 years, who were not eligible for vaccination, increased from 23.1% in the first survey to 87.1% in the fourth. The prevalence of active infection was below 10.1% in all surveys. A high seroprevalence to SARS-CoV-2 was observed in the study population, including individuals not eligible for vaccination at that time, particularly after circulation of the highly transmissible Delta variant. These data are important to inform decision making on the vaccination strategies in the context of pandemic slowdown in Mozambique.

Regulatory Compliance of Cosmetic Products in Kenya: A Narrative Review on Quality and Safety

This study reviews the regulatory compliance of cosmetic products in Kenya, with a focus on consumer risks and potential public health impact. A narrative review study design was employed and a literature search was conducted using government reports, publications, academic databases, and other relevant sources. Keywords such as cosmetic industry challenges and cosmetic regulation in Kenya were employed to search the literature for papers. Data focusing on regulatory frameworks, enforcement mechanisms, adverse reactions, and industry challenges was then extracted and discussed in the form of a narration. The growth of the cosmetic industry, fueled by urbanization, a rising middle class, and increased grooming awareness, has attracted both local and international investors, presenting challenges in maintaining product quality and safety standards. Seven cases of adverse reactions related to cosmetics were reported by Pharmacy and Poisons Board between 2018-2023. The current study identifies noteworthy challenges by examining Kenya’s regulatory framework, led by the Pharmacy and Poisons Board and the Kenya Bureau of Standards. Enforcement disparities within international standards, variance in regulations on prohibited ingredients, inconsistent labeling requirements, industry dynamics, and counterfeiting, compounded with the complex nature of safety assessments raise concerns for cosmetic regulators, distributors, and manufacturers. The study highlights existing gaps in regulatory oversight emphasizing the necessity of robust enforcement mechanisms. Compliance assessments by KEBS and PPB encompass the evaluation of ingredients, formulations, microbial contamination, packaging, and labeling. Factors contributing to regulatory noncompliance include poor product quality aggravated by supply chain complexities in the vast and diverse sector. The cosmetic cottage industry inadvertently presents a quality risk to consumers due to limited process and testing capacity. Reported adverse reactions, particularly to mercury-containing skin-lightening products, raise concerns about public health implications. This study advocates for continuous product monitoring and heightened vigilance. The review proposes improvement strategies, emphasizing a dynamic regulatory approach, periodic ingredient reviews, a centralized product approval system, and alignment with global manufacturing standards. Targeted consumer education initiatives focusing on product quality attributes, adverse reaction reporting, label reading, and counterfeit awareness are recommended.

Efficient detection of nitric oxide a biomarker associated with COVID19 via N, P co-doped C60 fullerene: a computational study.

Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations.

An optimization framework for large-scale screening under limited testing capacity with application to COVID-19.

We consider the problem of targeted mass screening of heterogeneous populations under limited testing capacity. Mass screening is an essential tool that arises in various settings, e.g., ensuring a safe supply of blood, reducing prevalence of sexually transmitted diseases, and mitigating the spread of infectious disease outbreaks. The goal of mass screening is to classify whole population groups as positive or negative for an infectious disease as efficiently and accurately as possible. Under limited testing capacity, it is not possible to screen the entire population and hence administrators must reserve testing and target those among the population that are most in need or most susceptible. This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations to target for screening. We conduct a comprehensive analysis that considers the two most commonly adopted schemes: Individual testing and Dorfman group testing. For both schemes, we formulate an optimization model that aims to minimize the number of misclassifications under a testing capacity constraint. By analyzing the formulations, we establish key structural properties which we use to construct efficient and accurate solution techniques. We conduct a case study on COVID-19 in the United States using geographic-based data. Our results reveal that the considered proactive targeted schemes outperform commonly adopted practices by substantially reducing misclassifications. Our case study provides important managerial insights with regards to optimal allocation of tests, testing designs, and protocols that dictate the optimality of schemes. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals

BackgroundThere is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing).MethodsTwo different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a ‘typical’ English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to ‘normal life’ without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the ‘ward’ testing scheme only healthcare workers who work on a single ward are eligible and in the ‘full’ testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers.ResultsIncluding a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests.ConclusionsWhether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.

Estimating the potential impact and diagnostic requirements for SARS-CoV-2 test-and-treat programs

Oral antivirals have the potential to reduce the public health burden of COVID-19. However, now that we have exited the emergency-phase of the COVID-19 pandemic, declining SARS-CoV-2 clinical testing rates (average testing rates = ≪\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ll$$\\end{document}10 tests/100,000 people/day in low-and-middle income countries; <100 tests/100,000 people/day in high-income countries; September 2023) make the development of effective test-and-treat programs challenging. We used an agent-based model to investigate how testing rates and strategies affect the use and effectiveness of oral antiviral test-to-treat programs in four country archetypes of different income levels and demographies. We find that in the post-emergency-phase of the pandemic, in countries where low testing rates are driven by limited testing capacity, significant population-level impact of test-and-treat programs can only be achieved by both increasing testing rates and prioritizing individuals with greater risk of severe disease. However, for all countries, significant reductions in severe cases with antivirals are only possible if testing rates were substantially increased with high willingness of people to seek testing. Comparing the potential population-level reductions in severe disease outcomes of test-to-treat programs and vaccination shows that test-and-treat strategies are likely substantially more resource intensive requiring very high levels of testing (≫100 tests/100,000 people/day) and antiviral use suggesting that vaccination should be a higher priority.

Utilizing river and wastewater as a SARS-CoV-2 surveillance tool in settings with limited formal sewage systems

The COVID-19 pandemic has profoundly impacted health systems globally and robust surveillance has been critical for pandemic control, however not all countries can currently sustain community pathogen surveillance programs. Wastewater surveillance has proven valuable in high-income settings, but less is known about the utility of water surveillance of pathogens in low-income countries. Here we show how wastewater surveillance of SAR-CoV-2 can be used to identify temporal changes and help determine circulating variants quickly. In Malawi, a country with limited community-based COVID-19 testing capacity, we explore the utility of rivers and wastewater for SARS-CoV-2 surveillance. From May 2020–May 2022, we collect water from up to 112 river or defunct wastewater treatment plant sites, detecting SARS-CoV-2 in 8.3% of samples. Peak SARS-CoV-2 detection in water samples predate peaks in clinical cases. Sequencing of water samples identified the Beta, Delta, and Omicron variants, with Delta and Omicron detected well in advance of detection in patients. Our work highlights how wastewater can be used to detect emerging waves, identify variants of concern, and provide an early warning system in settings with no formal sewage systems.

PalAST: A Cross-Platform Mobile Application for Automated Disk Diffusion Antimicrobial Susceptibility Testing

Antibiotic resistance is the ability of bacteria to resist the effects of antibiotics, making infections more difficult to treat and increasing the risk of complications and death. One way to fight antibiotic resistance is by identifying the most effective antibiotics for treating bacterial infections. This can be done through a laboratory test called AST, which is used to determine the susceptibility of bacteria to antibiotics. However, manual AST has several limitations that include time delay, limited accuracy, limited testing capacity, and subjective interpretation of results. Therefore, there is an emergent need for a more reliable and efficient alternative to manual AST. Recently, few works have tried to automate disk diffusion AST through AI-based solutions and mobile applications. However, these works do not support advanced analysis and interpretation of results, do not present evaluation of detection performance, or are not publicly available to download and use. This work proposes PalAST, a cross-platform mobile application that supports automated disk diffusion AST. The application enables biologists to take AST photos and analyze them in real time with minimal human intervention. It uses image processing and a pre-trained machine learning model to detect antibiotic disks in the agar plate and predict bounding circles for inhibition zones. Then, it provides an interpretation of results including the diameters of the inhibition zones, the labels on the antibiotic disks, and the rating of the bacteria as susceptible, intermediate, or resistant to each antibiotic. PalAST also stores the results of tests, allowing users to access and review past test results. PalAST was tested using a number of real AST photos, and the detection performance was evaluated by using common metrics, i.e. precision, recall, and Intersection over Union. We also used expert evaluation through a questionnaire to assess the usability and ease of use of PalAST.

Public Sector Response to COVID-19 Pandemic in India: Challenges and Lessons Learned

The COVID-19 pandemic has significantly impacted the public sector in India, leading to a range of challenges that have required swift and effective responses. This abstract provides an overview of the challenges faced by the public sector in India in responding to the pandemic and the lessons learned from the experience. The challenges faced by the public sector in India have included a shortage of medical supplies, equipment and hospital beds, as well as limited testing capacity and inadequate health infrastructure. Additionally, India's large population and the spread of the virus in densely populated urban areas have created additional challenges. Despite these challenges, the public sector has responded with a range of measures, including the establishment of COVID-19 hospitals and quarantine facilities, the provision of financial assistance to those affected by the pandemic, and the development of innovative solutions such as telemedicine and online consultation services. The experience of the COVID-19 pandemic has also highlighted a range of lessons for the public sector in India. These include the need for greater investment in healthcare infrastructure, the importance of effective communication and coordination between different levels of government, and the need for proactive planning and preparation for future pandemics. Overall, the response of the public sector in India to the COVID-19 pandemic has been a complex and challenging process, but it has also provided important lessons and insights for the future.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil.

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

Evolving swabbing practices for COVID-19 in a New Zealand emergency department during the early stages of an emerging pandemic.

To review if tests for suspected COVID-19 were performed according to the Ministry of Health (MoH) case definitions, identify patterns associated with testing outside of the case definition, and discuss the potential impacts on hospital services. This was a retrospective audit of patients presenting to the Wellington Hospital ED between 24 March 2020 and 27 April 2020 who were swabbed for COVID-19 in ED. Swabs were audited against the March 15th and April 8th MoH COVID-19 case definitions. Five hundred and thirty-six COVID-19 swabs for 518 patients were taken during the study period. There was poor alignment of testing with the March 15th case definition, with only 11.6% of the 164 swabs taken during this period meeting the case definition. Of the 145 swabs that did not meet the case definition, the majority (n = 119, 82.1%) met symptom criteria only. Alignment of testing with the wider April 8th case definition was much higher with 88.2% meeting criteria. Factors associated with being swabbed despite not meeting the case definitions included fever >38°, a diagnosis of cancer, subsequent hospital admission, and for the March case definition only 'contact with a traveller'. There were associations found between testing outside of criteria and specific variables potentially perceived as high-risk. Poor alignment of testing with case definitions can impact hospital services through the (mis)use of limited laboratory testing capacity and implications for resource management. Improved communication and feedback between clinicians and policymakers may improve case definition implementation in a clinical setting.

Recent outbreaks of Marburg virus in Africa: an urgent call for public concern

Recent outbreaks of Marburg virus in Africa: an urgent call for public concern

Symptoms Predicting SARS-CoV-2 Test Results in Resident Physicians and Fellows in New York City

Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in physician trainees from a large healthcare system in New York. We used survey data on symptoms history and SARS-CoV-2 testing results collected retrospectively from 328 physician trainees in the Mount Sinai Health System, over the period 1 February 2020 to 31 July 2020. Prospective data on symptoms reported prior to SARS-CoV-2 test results were available from the employee health service COVID-19 registry for 186 trainees and analyzed to confirm absence of recall bias. We estimated the associations between symptoms and IgG antibody and/or reverse transcriptase polymerase chain reaction test results using Bayesian generalized linear mixed effect regression models adjusted for confounders. We identified symptoms predicting a positive SARS-CoV-2 test result using extreme gradient boosting (XGBoost). Cough, chills, fever, fatigue, myalgia, headache, shortness of breath, diarrhea, nausea/vomiting, loss of smell, loss of taste, malaise and runny nose were associated with a positive SARS-CoV-2 test result. Loss of taste, myalgia, loss of smell, cough and fever were identified as key predictors for a positive SARS-CoV-2 test result in the XGBoost model. Inclusion of sociodemographic and occupational risk factors in the model improved prediction only slightly (from AUC = 0.822 to AUC = 0.838). Loss of taste, myalgia, loss of smell, cough and fever are key predictors for symptom-based screening of SARS-CoV-2 infection in healthcare settings with remote screening and/or limited testing capacity.

Wastewater surveillance of SARS-CoV-2 genomic populations on a country-wide scale through targeted sequencing.

SARS-CoV-2 surveillance of viral populations in wastewater samples is recognized as a useful tool for monitoring epidemic waves and boosting health preparedness. Next generation sequencing of viral RNA isolated from wastewater is a convenient and cost-effective strategy to understand the molecular epidemiology of SARS-CoV-2 and provide insights on the population dynamics of viral variants at the community level. However, in low- and middle-income countries, isolated groups have performed wastewater monitoring and data has not been extensively shared in the scientific community. Here we report the results of monitoring the co-circulation and abundance of variants of concern (VOCs) of SARS-CoV-2 in Uruguay, a small country in Latin America, between November 2020-July 2021 using wastewater surveillance. RNA isolated from wastewater was characterized by targeted sequencing of the Receptor Binding Domain region within the spike gene. Two computational approaches were used to track the viral variants. The results of the wastewater analysis showed the transition in the overall predominance of viral variants in wastewater from No-VOCs to successive VOCs, in agreement with clinical surveillance from sequencing of nasal swabs. The mutations K417T, E484K and N501Y, that characterize the Gamma VOC, were detected as early as December 2020, several weeks before the first clinical case was reported. Interestingly, a non-synonymous mutation described in the Delta VOC, L452R, was detected at a very low frequency since April 2021 when using a recently described sequence analysis tool (SAM Refiner). Wastewater NGS-based surveillance of SARS-CoV-2 is a reliable and complementary tool for monitoring the introduction and prevalence of VOCs at a community level allowing early public health decisions. This approach allows the tracking of symptomatic and asymptomatic individuals, who are generally under-reported in countries with limited clinical testing capacity. Our results suggests that wastewater-based epidemiology can contribute to improving public health responses in low- and middle-income countries.

Public sector response to COVID -19 Pandemic in India: Challenges and lessons learned

COVID-19 pandemic has had a significant impact on the public sector in India, leading to a range of challenges that have required swift and effective responses. This abstract provides an overview of the challenges faced by the public sector in India in responding to the pandemic, and the lessons learned from the experience. The challenges faced by the public sector in India have included a shortage of medical supplies, equipment and hospital beds, as well as limited testing capacity and inadequate health infrastructure. Additionally, India's large population and the spread of the virus in densely populated urban areas have created additional challenges. Despite these challenges, the public sector has responded with a range of measures, including the establishment of COVID-19 hospitals and quarantine facilities, the provision of financial assistance to those affected by the pandemic, and the development of innovative solutions such as telemedicine and online consultation services. The experience of the COVID-19 pandemic has also highlighted a range of lessons for the public sector in India. These include the need for greater investment in healthcare infrastructure, the importance of effective communication and coordination between different levels of government, and the need for proactive planning and preparation for future pandemics. Overall, the response of the public sector in India to the COVID-19 pandemic has been a complex and challenging process, but it has also provided important lessons and insights for the future.

Public sector response to COVID -19 Pandemic in India: Challenges and lessons learned

The COVID-19 pandemic has had a significant impact on the public sector in India, leading to a range of challenges that have required swift and effective responses. This abstract provides an overview of the challenges faced by the public sector in India in responding to the pandemic, and the lessons learned from the experience. The challenges faced by the public sector in India have included a shortage of medical supplies, equipment and hospital beds, as well as limited testing capacity and inadequate health infrastructure. Additionally, India's large population and the spread of the virus in densely populated urban areas have created additional challenges. Despite these challenges, the public sector has responded with a range of measures, including the establishment of COVID-19 hospitals and quarantine facilities, the provision of financial assistance to those affected by the pandemic, and the development of innovative solutions such as telemedicine and online consultation services. The experience of the COVID-19 pandemic has also highlighted a range of lessons for the public sector in India. These include the need for greater investment in healthcare infrastructure, the importance of effective communication and coordination between different levels of government, and the need for proactive planning and preparation for future pandemics. Overall, the response of the public sector in India to the COVID-19 pandemic has been a complex and challenging process, but it has also provided important lessons and insights for the future.

Pandemic Recessions and Contact Tracing

Abstract We study contact tracing in a new macro-epidemiological model with asymptomatic transmission and limited testing capacity. Contact tracing is a testing strategy that aims to reconstruct the infection chain of newly symptomatic agents. This strategy may be unsuccessful because of an externality leading agents to expand their interactions at rates exceeding policymakers’ ability to test all the traced contacts. Complementing contact tracing with timely deployed containment measures (e.g., social distancing or a tighter quarantine policy) corrects this externality and delivers outcomes that are remarkably similar to the benchmark case where tests are unlimited. We provide theoretical underpinnings to the risk of becoming infected in macro-epidemiological models. Our methodology to reconstruct infection chains is not affected by curse-of-dimensionality problems.

Limitation of Interpreting a Single COVID Cycle Threshold Value

Abstract The United States reported its first case of coronavirus disease 2019 (COVID-19) in January 2020. There was limited testing capacity at the time, and the causative agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was indeed extremely infectious. Testing, therefore, has become a vital part of controlling the COVID-19 pandemic. Nucleic acid amplification tests, such as polymerase chain reaction platform, a value called cycle threshold (Ct) is reported. This number represents the number of polymerase chain reaction test cycles it takes for the sample to become positive. It is inversely proportional to the amount of viral RNA present in the sample. Therefore, a high Ct value infers a low quantity of viral RNA, and conversely, a low Ct value implies high viral RNA, which in turn correlates with infectivity. When interpreted appropriately with the right clinical context, it could be used to assess the infectiousness of an individual. In September 2021, a 73-year-old man who previously completed a primary series of Moderna vaccine (2 injections) presented to the hospital with chest pain. Cardiac catheterization showed multivessel coronary artery disease and was deemed to have unstable angina. The plan was to undergo coronary aortic bypass graft surgery. Preoperative routine testing revealed a positive COVID-19 polymerase chain reaction test with a high Ct value. During the hospital stay, it was shown that he had a decreased Ct value and thus was determined to have an acute case of asymptomatic COVID-19 infection. His surgery was delayed according to the isolation protocol per Centers for Disease Control and Prevention for safety precautions. He eventually underwent coronary aortic bypass graft after completion of isolation and had an uneventful postoperative course. It was also documented that he had serological seroconversion consistent with acute COVID-19 infection. This case illustrates that reporting Ct values is useful in a certain clinical context, but clinicians need to be familiar with the transmissibility and history of SARS-CoV-2 infection as well as understanding the interpretation of positive tests. The appropriate clinical approach and management should be assessed based on exposure history, epidemiology, physical examination, radiographic assessment, and laboratory studies.

A Learning-Based Context-Aware Quality Test System in B5G-Aided Advanced Manufacturing

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.