Search

The pandemic of COVID-19 and its widespread transmission have made us realize the importance of early, quick diagnostic tests for facilitating effective cure and management. The primary obstacles encountered were accurately distinguishing COVID-19 from other illnesses including the flu, common cold, etc. While the polymerase chain reaction technique is a robust technique for the determination of SARS-CoV-2 in patients of COVID-19, there arises a high demand for affordable, quick, user-friendly, and precise point-of-care (POC) diagnostic in therapeutic settings. The necessity for available tests with rapid outcomes spurred the advancement of POC tests that are characterized by speed, automation, and high precision and accuracy. Paper-based POC devices have gained increasing interest in recent years because of rapid, low-cost detection without requiring external instruments. At present, microfluidic paper-based analysis devices have garnered public attention and accelerated the development of such POCT for efficient multistep assays. In the current review, our focus will be on the fabrication of detection modules for SARS-CoV-2. Here, we have included a discussion on various strategies for the detection of viral moieties. The compilation of these strategies would offer comprehensive insight into the detection of the causative agent preparedness for future pandemics. We also provide a descriptive outline for paper-based diagnostic platforms, involving the determination mechanisms, as well as a commercial kit for COVID-19 as well as their outlook. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has created an urgent need for rapid, effective, and low-cost SARS-CoV-2 diagnostic testing. Here, we describe COV-ID, an approach that combines RT-LAMP with deep sequencing to detect SARS-CoV-2 in unprocessed human saliva with a low limit of detection (5-10 virions). Based on a multi-dimensional barcoding strategy, COV-ID can be used to test thousands of samples overnight in a single sequencing run with limited labor and laboratory equipment. The sequencing-based readout allows COV-ID to detect multiple amplicons simultaneously, including key controls such as host transcripts and artificial spike-ins, as well as multiple pathogens. Here, we demonstrate this flexibility by simultaneous detection of 4 amplicons in contrived saliva samples: SARS-CoV-2, influenza A, human STATHERIN, and an artificial SARS calibration standard. The approach was validated on clinical saliva samples, where it showed excellent agreement with RT-qPCR. COV-ID can also be performed directly on saliva absorbed on filter paper, simplifying collection logistics and sample handling. [ABSTRACT FROM AUTHOR]

To optimize the emergency room environment, this article puts forward comprehensive diagnoses and recommendations to minimize healthcare- associated infections. Emergency rooms are usually the initial point of entry into the healthcare system for individuals with different illnesses and needs. These environments frequently operate at maximum capacity, which result in challenges related to spatial organization. Therefore, a Post-Occupancy Evaluation was conducted within such an environment during the COVID-19 pandemic, utilizing a combination of qualitative and quantitative data collection methods. The following methodologies were employed to achieve the research objectives and scope: semi-structured interviews with staff members and a specialized architect; walkthrough accompanied by key individuals; behavior observation for flow mapping and quantification; visual records and physical surveys; measurements of environmental conditions; and computational fluid dynamics simulations. The obtained results show the significance of maintaining and monitoring environmental conditions in specific environments; ensuring the appropriate allocation of hospital sectors; leveraging technology to reduce the exchange of paper among professionals; employing video calls to receive patients with flu symptoms; and implementing segregated patient-staff flow. Conclusively, these diagnoses and recommendations hold the potential to not only enhance the built environment of the case study but also to benefit other facilities with similar typologies. [ABSTRACT FROM AUTHOR]

Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide. Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission. The standard diagnostic approach is based on RT-PCR. However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions. Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice. Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans. This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis. We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19. Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism. In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance. Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%. [ABSTRACT FROM AUTHOR]

The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets. [ABSTRACT FROM AUTHOR]

The COVID-19 is a highly contagious infection that has already had a catastrophic effect on the most severe diseases, including death, worldwide. Blockchain-based healthcare systems are being introduced to help simplify mobile and telehealth services, reducing patient stress and the cost of critical clinical services. Shared the advantages of blockchain for building a cutting-edge authentication infrastructure and detecting COVID-19 suspicious cases. The authors presented a blockchain-based design for developing a real-time cellular health monitoring system for COVID-19 patients in this paper. This study identifies clinical problems and electronic diagnoses for people with COVID-19 infectious diseases and provides a framework for them. Any mobile application can be configured on digital devices such as smartphones. COVID-19 patients may benefit from such applications. Smartphone apps are designed to save time and money while increasing the efficiency of infectious patients. IoT and Blockchain strategies are presented in the four-layer structure. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan. Methods: Data was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan. Results: Pakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant. Conclusion: Each wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent. [ABSTRACT FROM AUTHOR]

This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of the same imaging characteristics as other common forms of bacterial and viral pneumonia. Differentiating COVID-19 from other common pulmonary infections is a non-trivial task. To help offset what commonly requires hours of tedious manual annotation, several innovative solutions have been published to help healthcare providers during the COVID-19 pandemic. However, the absence of a comprehensive survey on the subject makes it challenging to ascertain which approaches are promising and therefore deserve further investigation. In this survey, we present an in-depth review of deep learning techniques that have recently been applied to the task of discovering the diagnosis and prognosis of COVID-19 patients. We categorize existing approaches based on features such as dimensionality of radiological imaging, system purpose, and used deep learning techniques, underlying core issues, and challenges. We also address the merits and shortcomings of various approaches, and finally we discuss future directions for this research. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze Internet of Medical Things-driven remote monitoring systems, big healthcare data analytics, and wireless body area networks in COVID-19 detection and diagnosis. With increasing evidence of COVID-19 diagnostic applications, there is an essential demand for comprehending whether remote rapid monitoring and diagnosis of suspected COVID-19 individuals can be enabled by interconnected Internet of Medical Things infrastructure and healthcare equipment. In this research, prior findings were cumulated indicating that Internet of Medical Things can be leveraged in contact tracing during the COVID-19 pandemic. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout January 2022, with search terms including “COVID-19” + “Internet of Medical Things-driven remote monitoring systems,” “big healthcare data analytics,” and “wireless body area networks.” As I analyzed research published between 2020 and 2022, only 148 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 29, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

This research focuses on two main aspects, each equally important for tourism actors. First, we analyzed the impact of the Covid-19 pandemic on the rural mountain tourism in Romania and whether the resilience and sustainability test was passed. The second aim of the research was to identify solutions for the relaunch and development of the rural mountain tourism in Romania. Several research methods have been used to meet our objectives, such as analysis, synthesis, comparison, observation or documentary research. The quantitative research methodology used is limited to the questionnaire-based survey. The questionnaire contained 16 questions and was administered by telephone, between 15-30 March 2023 was addressed to a number of 100 respondents including owners or administrators of classified accommodation units in the mountain area. The analysis of the results shows that the pandemic had a neutral to positive effect for tourism in the mountain area and that, for the most part, the resilience and sustainability test was passed. We consider that this study is very useful for tourism entrepreneurs, for local and central public authorities and the conclusions and recommendations extracted from the questionnaires can suggest valuable ideas for the relaunch and development of the rural mountain tourism in Romania. [ABSTRACT FROM AUTHOR]

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual's health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P  < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]

This paper evaluates India's first officially approved self-administered rapid antigen test kit against COVID-19, a device called CoviSelf. The context is rural India. Rapid antigen tests (RATs) are currently popular in situations where vaccination rates are low, where sections of the community remain unvaccinated, where the COVID-19 pandemic continues to grow and where easy or timely access to RTPCR (reverse transcription-polymerase chain reaction) testing is not an option. Given that rural residents make up 66% of the Indian population, our evaluation focuses on the question of whether this self-administered RAT could help protect villagers and contain the Indian pandemic. CoviSelf has two components: the test and IT (information technology) parts. Using discourse analysis, a qualitative methodology, we evaluate the practicality of the kit on the basis of data in its instructional leaflet, reports about India's 'digital divide' and our published research on the constraints of daily life in Indian villages. This paper does not provide a scientific assessment of the effectiveness of CoviSelf in detecting infection. As social scientists, our contribution sits within the field of qualitative studies of medical and health problems. Self-administered RATs are cheap, quick and reasonably reliable. Hence, point-of-care testing at the doorsteps of villagers has much potential, but realising the benefits of innovative, diagnostic medical technologies requires a realistic understanding of the conditions in Indian villages and designing devices that work in rural situations. This paper forms part of a larger project regarding the COVID-19 pandemic in rural India. A follow-up study based on fieldwork is planned for 2022–2023. [ABSTRACT FROM AUTHOR]

In this study, we searched for the latest literature on the use of deep learning applications to combat COVID-19 and these were identified from several search engines including IEEE Xplore, Google Scholar, PubMed, and Scopus. This involved a comprehensive analysis of the studies to identify the challenges associated with the use of deep learning models with a view to highlight the possible future trends in the development of deep learning systems that are efficient and more reliable for the diagnosis of COVID-19 patients. This paper provides information related to the deep learning techniques used to detect COVID-19. This paper discusses the Convolutional Neural Networks’ (CNNs) structure, how to train CNNs, and highlights the different pre-trained models of CNNs that can be used for the detection of COVID-19. This paper explores the latest developments in the diagnosis of COVID-19 using deep learning applications that rely on the use of X-ray images taken from medical imaging samples. A review of the different models developed to facilitate effective diagnosis of COVID-19 provides information regarding the experimental data, the data splitting techniques used, as well as the proposed architecture for detecting COVID-19 and the different evaluation metrics for each model. This paper is a useful resource for medical and technical experts, as it helps them to develop a sound understanding of how deep learning techniques can be harnessed to stop the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Background: 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive. To alleviate the burden on the medical staff, we have done some research on the intelligent diagnosis of COVID-19.Methods: In this paper, we constructed a COVID-19 Diagnosis Ontology (CDO) by utilizing Protégé, which includes the basic knowledge graph of COVID-19 as well as diagnostic rules translated from Chinese government documents. Besides, SWRL rules were added into the ontology to infer intimate relationships between people, thus facilitating the efficient diagnosis of the suspected cases of COVID-19 infection. We downloaded real-case data and extracted patients' syndromes from the descriptive text, so as to verify the accuracy of this experiment.Results: After importing those real instances into Protégé, we demonstrated that the COVID-19 Diagnosis Ontology showed good performances to diagnose cases of COVID-19 infection automatically.Conclusions: In conclusion, the COVID-19 Diagnosis Ontology will not only significantly reduce the manual input in the diagnosis process of COVID-19, but also uncover hidden cases and help prevent the widespread of this epidemic. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers. [ABSTRACT FROM AUTHOR]

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) became a pandemic within a matter of months. Analysing the first year of the pandemic, data and surveillance gaps have subsequently surfaced. Yet, policy decisions and public trust in their country's strategies in combating COVID-19 rely on case numbers, death numbers and other unfamiliar metrics. There are many limitations on COVID-19 case counts internationally, which make cross-country comparisons of raw data and policy responses difficult. Purpose and conclusions: This paper presents and describes steps in the testing and reporting process, with examples from a number of countries of barriers encountered in each step, all of which create an undercount of COVID-19 cases. This work raises factors to consider in COVID-19 data and provides recommendations to inform the current situation with COVID-19 as well as issues to be aware of in future pandemics. [ABSTRACT FROM AUTHOR]

• Systematic mass testing in closed settings provides information on infection rates. • Test positivity was 11.6%, with only one-quarter reporting symptoms. • The prison wing handling new admissions reported the second-lowest positivity rate. • Testing uptake was higher in residents than in staff members. • Mass testing is a valuable tool to bring outbreaks under control quickly. The aim of this paper was to describe the results of mass asymptomatic testing for COVID-19 in a male prison in England following the declaration of an outbreak. It provides novel data on the implementation of a mass testing regime within a prison during the pandemic. The paper is an observational evaluation of the mass testing conducted for 6 months following the declaration of a COVID-19 outbreak within a prison. It investigated the incidence of positive cases in both staff and residents using polymerase chain reaction testing. Data from October 2020 until March 2021 was included. A total of 2170 tests were performed by 851 residents and 182 staff members; uptake was 48.3% for people living in prison and 30.4% for staff. Overall test positivity was 11.6% (14.3% for residents, 3.0% for staff), with around one-quarter of these reporting symptoms. The prison wing handling new admissions reported the second-lowest positivity rate (9.4%) of the eight wings. Mass testing for COVID-19 over a short space of time can lead to rapid identification of additional cases, particularly asymptomatic cases. Testing that relies on residents and staff reporting symptoms will underestimate the true extent of transmission and will likely lead to a prolonged outbreak. [ABSTRACT FROM AUTHOR]

COVID-19 made explicit the need for rethinking the way in which we conduct testing for epidemic emergencies. During the COVID-19 pandemic, the dependence on centralized lab facilities and resource-intensive methodologies (e.g., RT-qPCR methods) greatly limited the deployment of widespread testing efforts in many developed and underdeveloped countries. Here, we illustrate the development of a simple and portable diagnostic kit that enables self-diagnosis of COVID-19 at home from saliva samples. We describe the development of a do-it-yourself (DIY) incubator for Eppendorf tubes that can be used to conduct SARS-CoV-2 detection with competitive sensitivity and selectivity from saliva at home. In a proof-of-concept experiment, we assembled Eppendorf-tube incubators at our home shop, prepared a single-tube mix of reagents and LAMP primers in our lab, and deployed these COVID-19 detection kits using urban delivery systems (i.e., Rappifavor or Uber) to more than 15 different locations in Monterrey, México. This straightforward strategy enabled rapid and cost-effective at-home molecular diagnostics of SARS-CoV-2 from real saliva samples with a high sensitivity (100%) and high selectivity (87%). [ABSTRACT FROM AUTHOR]

On March 16, 2020, the Director‐General of the World Health Organization said: "We have a simple message to all countries—test, test, test." This seems like sound advice, but what if limiting the number of tests has a positive effect on infection control? Although this may rarely be the case, the possibility raises an important ethical question that is closely related to a central tension between deontological and consequentialist approaches to ethics. In this paper, we first argue that early during the COVID‐19 pandemic, Japan offers an interesting case because it experienced few deaths due to COVID while the number of tests was limited, suggesting that there may be cases in which low testing contributes to infection control indeed. After that, we examine deontological constraints on such a "low‐testing" policy, focusing on issues related to a supposed "right to know," which is a central issue in medical ethics in general. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic caused by the newly emerged Coronavirus was first detected in China in December 2019 and rapidly spread throughout the world. With the rapid spread of the disease, all countries involved in accordance with their capacities took necessary measures to identify and manage the disease. The first patient was diagnosed with COVID-19 in Iran in February 2020. Considering the significant role of the laboratory services in detection and management of the disease, planning and programming to provide access to these services was considered as a top priority for healthcare system in Iran. Adopting all existing laboratory capacities and providing necessary infrastructures for expanding COVID-19 laboratory diagnostics, resulted in establishment of a nationwide network of COVID-19 molecular diagnostic laboratories within the shortest possible time. In addition to expanding the capacity of laboratory services, improving the laboratory network's performance have been taken into serious consideration. In spite of the strict sanctions, the COVID-19 molecular diagnostic laboratories scaled up to more than 430 laboratories currently (September 2021), mostly are operating in the private sector. With considerable efforts, COVID-19 molecular laboratory network was established to provide nationwide diagnostic services. Taking into account the capacity for performing SARS CoV-2 Rapid Antigen testing, at the moment, we are able to provide more than 100,000 COVID-19 diagnostic tests per day. However, due to continuation of the pandemic and in accordance with the needs of the health system, it is necessary to plan and carry out more activities for diagnostic testing of COVID-19. The purpose of this paper is to introduce the process of establishing the COVID-19 laboratory diagnostic network to manage the COVID-19 pandemic in Iran. [ABSTRACT FROM AUTHOR]

Due to the recent pandemic caused by coronavirus disease 2019 (COVID-19), the lateral flow immunoassay used for its rapid antigen test is more popular than ever before. However, the history of the lateral flow immunoassay is about 60 years old, and its original purpose of use, such as a COVID-19 rapid antigen test or a pregnancy test, was the qualitative detection of a target analyte. Recently, the demand for quantitative analysis of lateral flow immunoassays is increasing in various fields. Lateral flow immunoassays for quantitative detection using various materials and sensor technologies are being introduced, and readers for analyzing them are being developed. Quantitative analysis readers are highly anticipated for their future development in line with technological advancements such as optical, magnetic field, photothermal, and electrochemical sensors and trends such as weight reduction, miniaturization, and cost reduction of systems. In addition, the sensing, processing, and communication functions of portable personal devices such as smartphones can be used as tools for the quantitative analysis of lateral flow immunoassays. As a result, lateral flow immunoassays can efficiently achieve the goal of rapid diagnosis by point-of-care testing. Readers used for the quantification of lateral flow immunoassays were classified according to the adopted sensor technology, and the research trends in each were reviewed in this paper. The development of a quantitative analysis system was often carried out in the assay aspect, so not only the readers but also the assay development cases were reviewed if necessary. In addition, systems for quantitative analysis of COVID-19, which have recently been gaining importance, were introduced as a separate section. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic has globally impacted nursing education, particularly clinical learning opportunities for undergraduate nursing students. Purpose: In this paper, we report on an educational activity wherein students participated in a COVID‐19 Rapid Antigen Testing (RAT) clinic on a Canadian university campus. Methods: Between February–April 2021, in the second term of a five‐term accelerated program, nursing students (n = 60) participated in a nurse‐led COVID‐19 RAT clinic for students and staff living or working in congregate housing. Students participated in education activities which exposed nursing students to the full range of community health nursing roles in a pandemic. Results: From clinical, research, policy, and public health, this educational activity acted as a microcosm of the critical roles that nurses employ in the health ecosystem. Conclusion: We offer lessons learned about implementing this activity, and how these lessons can be applied to routine and exceptional nursing curriculum. [ABSTRACT FROM AUTHOR]

In order to slow the spread of the CoViD-19 pandemic, governments around the world have enacted a wide set of policies limiting the transmission of the disease. Initially, these focused on non-pharmaceutical interventions; more recently, vaccinations and large-scale rapid testing have started to play a major role. The objective of this study is to explain the quantitative effects of these policies on determining the course of the pandemic, allowing for factors like seasonality or virus strains with different transmission profiles. To do so, the study develops an agent-based simulation model, which explicitly takes into account test demand and behavioral changes following positive tests. The model is estimated using data for the second and the third wave of the CoViD-19 pandemic in Germany. The paper finds that during a period where vaccination rates rose from 5 to 40%, seasonality and rapid testing had the largest effect on reducing infection numbers. Frequent large-scale rapid testing should remain part of strategies to contain CoViD-19; it can substitute for many non-pharmaceutical interventions that come at a much larger cost to individuals, society, and the economy. [ABSTRACT FROM AUTHOR]

Hoping to find genomic clues linked to COVID-19 and end the pandemic has driven scientists' tremendous efforts to try all kinds of research. Signs of progress have been achieved but are still limited. This paper intends to prove the existence of at least three genomic signature patterns and at least seven subtypes of COVID-19 driven by five critical genes (the smallest subset of genes) using three blood-sampled datasets. These signatures and subtypes provide crucial genomic information in COVID-19 diagnosis (including ICU patients), research focuses, and treatment methods. Unlike existing approaches focused on gene fold-changes and pathways, gene-gene nonlinear and competing interactions are the driving forces in finding the signature patterns and subtypes. Furthermore, the method leads to high accuracy with hospitalized patients, showing biological and mathematical equivalences between COVID-19 status and the signature patterns and a methodological advantage over other methods that cannot lead to high accuracy. As a result, as new biomarkers, the new findings and genomic clues can be much more informative than other findings for interpreting biological mechanisms, developing the second (third) generation of vaccines, antiviral drugs, and treatment methods, and eventually bringing new hopes of an end to the pandemic. [ABSTRACT FROM AUTHOR]

In the event of a virus outbreak such as Covid‐19, testing is key. However, long waiting lines at testing facilities often discourage individuals from getting tested. This paper utilizes queueing‐game‐theoretic models to study how testing facilities should set scheduling and pricing policies to incentivize individuals to test, with the goal to identify the most cases of infection. Our findings are as follows. First, under the first‐in‐first‐out discipline (FIFO), the common practice of making testing free attracts the most testees but may not catch the most cases. Charging a testing fee may surprisingly increase case detection. Second, even though people who show symptoms are more likely to carry the virus, prioritizing these individuals over asymptomatic ones (another common practice) may let more cases go undetected than FIFO testing does. Third, we characterize the optimal scheduling and pricing policy. To maximize case detection, testing can be made free but one should also (partially) prioritize individuals with symptoms when testing demand is high and switch to (partially) prioritize the asymptomatic when testing demand is moderately low. [ABSTRACT FROM AUTHOR]

Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal s e n s y of 0.9422, s p e c y of 0.9466, p r e c n of 0.9494, a c c y of 0.9429, and F 1 s c o r e of 0.9394. [ABSTRACT FROM AUTHOR]

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large. [ABSTRACT FROM AUTHOR]

COVID-19 constitutes one of the devastating pandemics plaguing humanity throughout the centuries; within about 18 months since its appearing, the cumulative confirmed cases hit 173 million, whereas the death toll approaches 3.72 million. Although several vaccines became available for the public worldwide, the speed with which COVID-19 is spread, and its different mutant strains hinder stopping its outbreak. This, in turn, prompting the desperate need for devising fast, cheap and accurate tools via which the disease can be diagnosed in its early stage. Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the mainstay tool used to detect the COVID-19 symptoms. However, due to the high false-negative rate of this test, physicians usually use chest radiographs as an adjunct or alternative tool. Although radiographs screening is wide-available, low-cost, and its results are timely, relying on radiologists to interpret them manually stands against using radiographs as a diagnostic tool. Motivated by the need to speed up the radiographic diagnosis of COVID-19 and to improve its reliability, this paper proposes a novel deep-learning framework dubbed Parallel Deep Neural Networks for COVID-19 Diagnosis (PCN2). PCN2 treats the radiographs and their metadata simultaneously by running two CNNs in parallel. Firstly: a 2- dimensional CNN (2DCNN) to capture the spatial information from the radiographs due to its super competency in this domain. Secondly, a 1-dimensional CNN (1DCNN) to extract the medical knowledge presented in the metadata. By this integration, PCN2 can make perfect classifications even for those cases in which the infection signs in radiographs are unclear due to being the disease in early-stage, confounded by other markers or overlapped by other diseases. Extensive assessments of PCN2 carried out using several datasets demonstrate average diagnostic accuracy of 99.9 and 0.99 F1-score. [ABSTRACT FROM AUTHOR]

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus. [ABSTRACT FROM AUTHOR]

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method's precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is a global public health crisis that is now impacted by the COVID-19 pandemic. Little is known how COVID-19 risks influence people to consume antibiotics, particularly in contexts like Bangladesh where these pharmaceuticals can be purchased without a prescription. This paper identifies the social drivers of antibiotics use among home-based patients who have tested positive with SARS-CoV-2 or have COVID-19-like symptoms. Using qualitative telephone interviews, the research was conducted in two Bangladesh cities with 40 participants who reported that they had tested positive for coronavirus (n = 20) or had COVID-19-like symptoms (n = 20). Our analysis identified five themes in antibiotic use narratives: antibiotics as 'big' medicine; managing anxiety; dealing with social repercussions of COVID-19 infection; lack of access to COVID-19 testing and healthcare services; and informal sources of treatment advice. Antibiotics were seen to solve physical and social aspects of COVID-19 infection, with urgent ramifications for AMR in Bangladesh and more general implications for global efforts to mitigate AMR. [ABSTRACT FROM AUTHOR]

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies. Author summary: The global spread of SARS-CoV-2 and the strategies used to manage it have come at significant societal costs. We analyze how mixed control strategies, which utilize interventions that prevent new infections from occurring (e.g., distancing or shut-downs) and others that actively search for and isolate existing infections (here, mass testing), can achieve improved public health outcomes while avoiding severe socio-economic burdens. Our results suggest that increasing testing capacity, including the number of tests available and the speed at which test results are provided, can reduce reliance on costly preventative interventions. Such reduction is possible with more isolation of active infections, including those without reported symptoms. However, failing to maintain preventative interventions without sufficient testing capacity can lead to large increases in infection burdens. By defining the combined effect of these interventions through mathematical models, this study provides insight into relaxation of distancing measures, and lays the groundwork for future public health economics analyses on the cost-effectiveness of combined management strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has a significant negative effect on people's health, as well as on the world's economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers' attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure. [ABSTRACT FROM AUTHOR]

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy. [ABSTRACT FROM AUTHOR]

Early classification of the Coronavirus disease (COVID-19) is necessary to control its rapid spread and save patients’ lives. The fast spread of COVID-19 has increased the diagnostic encumbrance of radiologists. Therefore, clinicians need to quickly assess if a patient has COVID-19 or not. Artificial Intelligence (AI) has shown promising results in healthcare. So, this paper proposed a computer-aided intelligence model that can identify positive COVID-19 cases. It presented the pipeline of medicinal imaging and examination methods involved in COVID-19 image acquirement, segmentation, and diagnosis, using Computed Tomography (CT) images. This paper introduced two effective models for single machine learning (SML) and ensemble machine learning (EML) with 10-fold cross validation, to detect cases of COVID-19. The first classification model (SML) was applied with different algorithms, such as Decision Tree (DT), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). Results showed that the performance of the SVM surpassed other classifiers with a 98.85 % accuracy. The second classification model (EML) was applied with several algorithms, such as Random Forest (RF), Voting, and Bagging, to increase its accuracy up to 99.60%, especially using the Bagging classifier. Finally, the results of the two proposed models showed better performance compared with other recent studies. However, the EML showed an even better performance than SML and is recommended for use in real-time. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has had an immense and worldwide impact. In light of future pandemics or subsequent waves of COVID‐19 it is crucial to focus on the ethical issues that were and still are raised in this COVID‐19 crisis. In this paper, we look at issues that are raised in the testing and tracing of patients with COVID‐19. We do this by highlighting and expanding on an approach suggested by Fineberg that could serve as a public health approach. In this way, we highlight several ethical issues. As regards testing, questions are raised such as whether it is ethical to use less reliable tests in order to increase testing capacity or minimize harm for patients. Another issue is how wide testing should be and whether selective testing is in accordance with principles of social justice. Patients who have recovered from COVID‐19 might have some degree of immunity but attributing certain 'immunopriviliges' raises ethical questions. The use of various tracing methodologies (mobile apps or databases and trained tracers) raised evident questions of social justice and privacy. We argue why it is key to always uphold a test of proportionality where a fair balance must be sought. [ABSTRACT FROM AUTHOR]

The novel coronavirus COVID-19 has known a large spread over the globe threatening human health. Recommendations from WHO and specialists insist on testing on a mass scale. However, health systems do not have enough resources. The current process requires the isolation of testees in the hospitals' isolation rooms for several hours until the test results are revealed, limiting hospitals' capacities to test large numbers of cases. The aim of this paper was to estimate the impact of reducing the COVID-19 test time on controlling the pandemic spread, through increasing hospitals' capacities to test on a mass scale. First, a discrete-event simulation was used to model and simulate the COVID-19 testing process in Morocco. Second, a mathematical model was developed to demonstrate the effect of accurate identification of infected cases on controlling the disease's spread. Simulation results showed that hospitals' testing capacities could be increased six times if the test duration fell from 10 hours to 10 minutes. The reduction of test time would increase testing capacities, which help to identify all the infected cases. In contrast, the simulation results indicated that if the infected population is not accurately identified and no precautionary measures are taken, the virus will continue to spread until it reaches the total population. Reducing test time is a vital component of the response to the COVID-19 pandemic. It is essential for the effective implementation of policies to contain the virus. [ABSTRACT FROM AUTHOR]

Background: Different adaptation and changes have been practiced during COVID-19 pandemic. Objective: In this paper we investigated the impact of the COVID-19 pandemic on service delivery in the department of interventional neurology and adaptation of the department to the changed environment. Methodology: This was a retrospective analysis of diagnostic digital subtraction angiography (DSA) procedures done from January 2018 to December 2020 and were analyzed to detect significant breaks in time trend. Results: A total of 358, 426 and 251 patients got admitted for DSA in consecutive three years from 2018 to 2020 respectively. There was a sudden drop in the number of DSA procedures from 30 to 50 patients per month in the pre-COVID era before March 2020 to less than 10 patients per month during the COVID period of March to June 2020. However, the situation gradually improved following the introduction of RT-PCR test for SARS CoV-2 in June 2020. A poison regression showed a significant increase in monthly DSA procedures in the year 2019 compared to the year 2018, but a significant decrease in the pandemic year of 2020. Conclusion: By incorporating COVID-19 testing as a pre-requisite test before DSA procedures, the department of interventional neurology recovered from experiencing a significant drop in the number of performed DSA procedures in the initial periods of the pandemic to reaching a level observed in the pre-COVID era. [ABSTRACT FROM AUTHOR]

Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants. [ABSTRACT FROM AUTHOR]

Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the dataset. Given the prevalence of COVID-19 cases and the extensive volume of patient records lacking labels, this communication underscores our unique approach—conducting the first study on COVID-19 case diagnosis in an unsupervised manner. Our work operates under the assumption of prior knowledge regarding the number of classes, such as COVID-19, pneumonia, and normal, in a case study. By adopting an unsupervised learning paradigm, we leverage the wealth of unlabeled data, reducing dependence on human experts for annotating numerous images. This paper introduces an enhanced version of a recent direct method where non-negative cluster indices and spectral embeddings are jointly estimated. Beyond the inherent advantages of this method, our proposed model introduces improvements through two additional types of constraints: (i) ensuring consistent smoothing of cluster labels across all views and (ii) imposing an orthogonality constraint on the matrix of cluster assignments. The efficacy of the proposed method is demonstrated using the public COVIDx dataset with three classes, showcasing promising results in categorizing radiographs. The proposed approach is tested on other public image datasets to assess its effectiveness. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has lasted for over 2 years and has resulted in more than 486 million cases and over 6 million deaths worldwide. Pregnant women are a special group of society exposed to severe or unpredictable course of infection. Purpose of work: Assessment of awareness and willingness to vaccinate pregnant women against COVID-19 and reasons influencing this decision. Materials and methods: The proprietary online survey addressed to women in Poland during pregnancy and women who were pregnant in the period from December 27, 2020 (introduction of the vaccine in Poland) to 20.03.2022. The study included a total of 518 respondents of reproductive age, among which 398 have a university degree and 108 have secondary education. 376 respondents got vaccinated against COVID-19 -- 120 before getting pregnant, 199 during pregnancy and 57 after giving birth. Summary: Most of the surveyed women (321) hadn't had a positive PCR test for COVID-19 until the day of the survey, but 38% had a positive PCR test. Asked about reasons for getting vaccinated during pregnancy, most of the respondents stated that people should get vaccinated against COVID-19 in general, they got vaccinated because of the baby's health, and that they acted according to the recommendations of The Polish Society of Gynecologists and Obstetricians and the World's Health Organization. On the other hand, most of the women didn't get vaccinated because they were afraid of the side effects, there is not enough research on the topic, and they believe that the vaccination is ineffective. Additionally, 154 respondents were encouraged by the doctors to get vaccinated in opposite to 21 who were discouraged by the doctor. The majority of the respondents are aware of the great risk of COVID-19 infection during pregnancy. Therefore, they got vaccinated against COVID-19 to minimize the risks of the infection. [ABSTRACT FROM AUTHOR]

Background: Since the Coronavirus disease 2019 (COVID-19) pandemic began, the number of individuals recovering from COVID-19 infection have increased. Post-COVID Syndrome, or PCS, which is defined as signs and symptoms that develop during or after infection in line with COVID-19, continue beyond 12 weeks, and are not explained by an alternative diagnosis, has also gained attention. We systematically reviewed and determined the pooled prevalence estimate of PCS worldwide based on published literature. Methods: Relevant articles from the Web of Science, Scopus, PubMed, Cochrane Library, and Ovid MEDLINE databases were screened using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-guided systematic search process. The included studies were in English, published from January 2020 to April 2024, had overall PCS prevalence as one of the outcomes studied, involved a human population with confirmed COVID-19 diagnosis and undergone assessment at 12 weeks post-COVID infection or beyond. As the primary outcome measured, the pooled prevalence of PCS was estimated from a meta-analysis of the PCS prevalence data extracted from individual studies, which was conducted via the random-effects model. This study has been registered on PROSPERO (CRD42023435280). Results: Forty eight studies met the eligibility criteria and were included in this review. 16 were accepted for meta-analysis to estimate the pooled prevalence for PCS worldwide, which was 41.79% (95% confidence interval [CI] 39.70–43.88%, I2 = 51%, p = 0.03). Based on different assessment or follow-up timepoints after acute COVID-19 infection, PCS prevalence estimated at ≥ 3rd, ≥ 6th, and ≥ 12th months timepoints were each 45.06% (95% CI: 41.25–48.87%), 41.30% (95% CI: 34.37–48.24%), and 41.32% (95% CI: 39.27–43.37%), respectively. Sex-stratified PCS prevalence was estimated at 47.23% (95% CI: 44.03–50.42%) in male and 52.77% (95% CI: 49.58–55.97%) in female. Based on continental regions, pooled PCS prevalence was estimated at 46.28% (95% CI: 39.53%-53.03%) in Europe, 46.29% (95% CI: 35.82%-56.77%) in America, 49.79% (95% CI: 30.05%-69.54%) in Asia, and 42.41% (95% CI: 0.00%-90.06%) in Australia. Conclusion: The prevalence estimates in this meta-analysis could be used in further comprehensive studies on PCS, which might enable the development of better PCS management plans to reduce the effect of PCS on population health and the related economic burden. [ABSTRACT FROM AUTHOR]

Background: Arbaeen in Iraq has been one of the largest mass gatherings during the COVID-19 pandemic with 14.5 million attendees in 2020. We set out to assess the prevalence of current or past COVID-19 among 2020 Arbaeen participants, and establish associations between COVID-19 test results, symptoms, and known recent exposure. Methods: This was a cross-sectional study involving participants who joined Arbaeen walk in Iraq in October 2020. COVID-19 PCR and/or rapid antibody test were conducted among consented participants. A short questionnaire was administered. Rapid antibody testing was done onsite. Nasal and throat swab samples were transferred to the laboratory for PCR testing. Results: A total of 835 (88.3% male; 11.7% female) participants were recruited. The most common symptom overall was cough (9.6%) followed by sore throat, fever, and loss of taste/smell (6.6%, 5.5%, and 5.0%, respectively). One in five (20.3%) participants reported close contact with a confirmed COVID-19 case in the past 14 days. Of the 237 participants with a PCR test, 18 (7.6%) were positive. Of the 765 participants with rapid antibody test, 19.3% tested positive for IgM, 39.3% for IgG, and 16.4% for both. Approximately 40% of the participants had evidence of current or past COVID-19 infection based on antibody and PCR. Conclusions: The almost 1 in 10 COVID-19 cases within such a multimillion person gathering, illustrates the difficulty in limiting the participation of infectious individuals in religious mass gatherings. There is a pressing need to explore measures to reduce the risk of transmission of infectious diseases at major mass gathering events. [ABSTRACT FROM AUTHOR]

In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI. [ABSTRACT FROM AUTHOR]

This study aims to determine the attitudes and opinions of pre-service IT teacher candidates regarding online testing at the first period of the COVID-19 pandemic. This research is a descriptive study with explanatory sequential mixed method design. The study sample consisted of 69 teacher candidates enrolled in 3 different courses lectured by the researcher at the Computer Education and Instructional Technologies Department, in the spring semester of 2019-2020 academic year. The data were collected through the Online Examination Assessment Scale and an interview form developed by researcher. In quantitative data analysis, frequency, percentage, and average, independent sample t-test, and one-way ANOVA were applied as well as descriptive statistics. The content analysis method was used to analyze the qualitative data obtained from the survey. The findings suggest that (1) teacher candidates' attitude scores were above average; (2) their attitudes towards online testing differed significantly depending on gender, age, course, and computer self-efficacy levels; and (3) the participants who had taken online tests before had a more positive attitude towards online testing, but this difference was not significant. Besides, according to teacher candidates' opinions towards online testing, the most important advantage of this type of testing was that they felt independent and comfortable due to taking the test in the home environment and without a proctor. On the other hand, possible technical problems during the test were one of the frequently mentioned issues in the student views regarding online testing. [ABSTRACT FROM AUTHOR]