Early Detection Of COVID-19 Research Articles

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19.

This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases. The framework utilizes a multi-access edge computing technology such that end-user can access available resources as well the CNN on the cloud. Early detection of COVID-19 can improve treatment and mitigate transmission. During peaks of infection, hospitals worldwide have suffered from heavy patient loads, bed shortages, inadequate testing kits and short-staffing problems. Due to the time-consuming nature of the standard RT-PCR test, the lack of expert radiologists, and evaluation issues relating to poor quality images, patients with severe conditions are sometimes unable to receive timely treatment. It is thus recommended to incorporate computational intelligence methodologies, which provides highly accurate detection in a matter of minutes, alongside traditional testing as an emergency measure. CNN has achieved extraordinary performance in numerous computational intelligence tasks. However, finding a systematic, automatic and optimal set of hyperparameters for building an efficient CNN for complex tasks remains challenging. Moreover, due to advancement of technology, data are collected at sparse location and hence accumulation of data from such a diverse sparse location poses a challenge. In this article, we propose a framework of computational intelligence-based algorithm that utilize the recent 5G mobile technology of multi-access edge computing along with a new CNN-model for automatic COVID-19 detection using raw chest X-ray images. This algorithm suggests that anyone having a 5G device (e.g., 5G mobile phone) should be able to use the CNN-based automatic COVID-19 detection tool. As part of the proposed automated model, the model introduces a novel CNN structure with the genetic algorithm (GA) for hyperparameter tuning. One such combination of GA and CNN is new in the application of COVID-19 detection/classification. The experimental results show that the developed framework could classify COVID-19 X-ray images with 98.48% accuracy which is higher than any of the performances achieved by other studies.

Evaluation of Rapid Antigen Tests Using Nasal Samples to Diagnose SARS-CoV-2 in Symptomatic Patients.

IntroductionThe best way to mitigate an outbreak besides mass vaccination is via early detection and isolation of infected cases. As such, a rapid, cost-effective test for the early detection of COVID-19 is required.MethodsThe study included 4,183 mildly symptomatic patients. A nasal and nasopharyngeal sample obtained from each patient was analyzed to determine the diagnostic ability of the rapid antigen detection test (RADT, nasal swab) in comparison with the current gold-standard (RT-PCR, nasopharyngeal swab).ResultsThe calculated sensitivity and specificity of the RADT was 82.1 and 99.1%, respectively. Kappa's coefficient of agreement between the RADT and RT-PCR was 0.859 (p < 0.001). Stratified analysis showed that the sensitivity of the RADT improved significantly when lowering the cut-off RT-PCR Ct value to 24.ConclusionOur study's results support the potential use of nasal swab RADT as a screening tool in mildly symptomatic patients, especially in patients with higher viral loads.

A study of using cough sounds and deep neural networks for the early detection of Covid-19

The current clinical diagnosis of COVID-19 requires person-to-person contact, needs variable time to produce results, and is expensive. It is even inaccessible to the general population in some developing countries due to insufficient healthcare facilities. Hence, a low-cost, quick, and easily accessible solution for COVID-19 diagnosis is vital. This paper presents a study that involves developing an algorithm for automated and noninvasive diagnosis of COVID-19 using cough sound samples and a deep neural network. The cough sounds provide essential information about the behavior of glottis under different respiratory pathological conditions. Hence, the characteristics of cough sounds can identify respiratory diseases like COVID-19. The proposed algorithm consists of three main steps (a) extraction of acoustic features from the cough sound samples, (b) formation of a feature vector, and (c) classification of the cough sound samples using a deep neural network. The output from the proposed system provides a COVID-19 likelihood diagnosis. In this work, we consider three acoustic feature vectors, namely (a) time-domain, (b) frequency-domain, and (c) mixed-domain (i.e., a combination of features in both time-domain and frequency-domain). The performance of the proposed algorithm is evaluated using cough sound samples collected from healthy and COVID-19 patients. The results show that the proposed algorithm automatically detects COVID-19 cough sound samples with an overall accuracy of 89.2%, 97.5%, and 93.8% using time-domain, frequency-domain, and mixed-domain feature vectors, respectively. The proposed algorithm, coupled with its high accuracy, demonstrates that it can be used for quick identification or early screening of COVID-19. We also compare our results with that of some state-of-the-art works.

A cross-sectional study of knowledge of, attitude towards COVID-19 and prevalence of COVID-19 symptoms among undergraduate students of Niger Delta University, Bayelsa State, Nigeria

This study aimed to assess the levels of Knowledge, Attitude and Prevalence of COVID-19 symptoms among students of Niger Delta University in Bayelsa State of Nigeria. It was a descriptive cross-sectional study. Data was collected using convenience sampling among 350 participants drawn from the faculties of Pharmacy [75], Nursing [80], Management science [150] and Arts [45]. Data was collected with well-structure questionnaires that addressed the objectives of the study. All analyses were performed using SPSS version 23. Participation in this survey was anonymous, consensual and voluntary with informed consent given by all prospective respondents. Most respondents were within the age 18-25 years (63%); more males (55%); of the IJAW ethnic group (55%). All respondents had heard of Covid-19; 99% opined that Covid 19 is more prevalent in old individual; only 9% agreed that Covid-19 has a viral origin. Respondents identified the following as symptoms of Covid 19: Fever (100%), Cough (85%). Sore-throat (67%), Headache (43%). All respondents agreed that Covid-19 can be transmitted through touching the face, eyes, mouth; through contact with infected surfaces and through contact with individuals suspected to be infected. The majority of the respondents (85%) had a positive attitude towards Covid-19; all respondents opined that early detection of Covid-19 can improve treatment and outcome; that Covid-19 can be avoided by proper precautions (95%); if there is an available vaccine for the disease, it should be used (91%) and that health education can help prevent Covid-19 (71%). During the Peak, all respondents reportedly experienced unusual intense fatigue, muscle pain or stiffness; other experienced symptoms included shortness of breath (85%), loss of taste (84%), a runny nose or nasal congestion of unknown cause (77%), sudden loss of smell (77%), Diarrhea (77%), unusual headache (70%), sore throat (69%), fever (60%), flu-like chills (57%) and cough (41%) Overall, participants demonstrated a good knowledge of Covid-19 and most of the participants exhibited good attitudes towards Covid-19. The study was able to highlight gaps in specific aspects of knowledge and attitude of the students towards Covid-19 that should be focused on in future awareness and educational campaigns.

Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.

Background:COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.Methods:A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.Results:We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%).Conclusion:This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task. We used a publicly open CXR image dataset and implemented the detection model with task-specific pre-processing and near 80:20 split. This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597, which shall help better decision-making for various aspects of identification and treat the infection.

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19.

The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

Classification of High-Resolution Computed Tomography Manifestations for COVID-19 Patients: A Systematic Review

Context: The early detection of COVID-19 is of paramount importance for the disease treatment and control. As real-time reverse-transcription polymerase chain reaction indicates a low sensitivity, the computed tomography of patients' chest can play an effective role in the diagnosis of COVID-19, particularly for patients with false-negative RT-PCR tests. It is also effective in monitoring the clinical trends and assessing the severity of the disease. Objectives: Accordingly, this study aimed to review the different manifestations of the COVID-19 infections in High-Resolution Computed Tomography images of patients' chests and analyze the distribution of the disease in the lungs. The results can contribute to providing a comprehensive and concise reference on the appearance of various types of involvement and lung lesions and the extent of these lesions in the COVID-19 patients. Data Sources: We systematically searched four major indexing databases (namely PubMed, Science Direct, Google Scholar, and Cochrane Central) for articles published by May 2021 using the following keywords: High-Resolution Computed Tomography (HRCT), COVID-19, and Manifestations. Results: Overall, 29 studies addressing the role of HRCT in detecting and evaluating the manifestations of the COVID-19 infection in patients' lungs as ground glass opacification (GGO), consolidation, irregular solid nodules, fibrous stripes, crazy paving pattern, air bronchogram sign, etc. were reviewed. Conclusions: GGO was the most common finding, as reported in 96.6% of the reviewed articles, followed by consolidations (65.5%) and irregular solid nodules (55.2%). Most patients revealed the disease process as a bilateral distribution in the peripheral areas of the lung.

Evaluation of the Abbott BinaxNOW COVID-19 Test Ag Card for rapid detection of SARS-CoV-2 infection by a local public health district with a rural population.

SARS-CoV-2 RT-PCR, the gold standard for diagnostic testing, may not be readily available or logistically applicable for routine COVID-19 testing in many rural communities in the United States. In this validation study, we compared the BinaxNOW™ COVID-19 Test Ag Card with SARS-CoV-2 RT-PCR in 214 participants who sought COVID-19 testing from a local public health district in Idaho, USA. The median age of participants was 35 and 82.7% were symptomatic. Thirty-seven participants (17.3%) had positive RT-PCR results. Results between the two tests were 94.4% concordant. The sensitivity of the BinaxNOW™ COVID-19 Test Ag Card was 67.6% (95% CI: 50.2-81.9%), and the specificity was 100.0% (95% CI: 97.9-100.0%). The positive predictive value (PPV) for the BinaxNOW™ COVID-19 Test Ag Card was 100.0% (95% CI: 86.2-100.0%), and the negative predictive value (NPV) was 93.6% (95% CI: 89.1-96.6%). Although the sensitivity of BinaxNOW™ COVID-19 Test Ag Card was lower than RT-PCR, rapid results and high specificity support its use for early detection of COVID-19, especially in settings where SARS-CoV-2 RT-PCR testing is not readily available. Rapid antigen tests, such as the BinaxNOW™ COVID-19 Test Ag Card, may be a more convenient tool in quickly identifying and preventing COVID-19 transmission, especially in rural settings.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective: The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them. Materials and Methods: A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles. Results: A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students. Conclusion: The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network

Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.

An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images.

Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of 0.97 and 0.93, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.

Diagnostic accuracy of a rapid diagnostic test for the early detection of COVID-19.

ObjectivesThis study was undertaken to evaluate the diagnostic performance of the BinaxNOW COVID-19 Ag Card rapid antigen assay (Abbott; Chicago, IL, USA) in the detection of COVID-19 infection compared to the reference standard of PCR testing.MethodsWe evaluated the BinaxNOW COVID-19 Ag Card rapid antigen assay relative to a standard reference PCR test. We tested 3810 nasal swabs from symptomatic and asymptomatic adults undergoing surveillance COVID-19 testing at Howard University using one swab for each nostril. One swab was tested using the rapid antigen assay and the other using the PCR test.ResultsThe sensitivity of the BinaxNOW COVID-19 Ag Card rapid antigen assay was 91.84% (95% confidence interval (CI): 80.40–97.73%) and the specificity was 99.95% (95% CI: 99.81–99.99%). The range of Ct values for the N gene was 10.74–34.90 (M = 26.88, SD=4.86). Fourteen (28.6%) samples had an N gene Ct value > 30. The average N gene Ct value for rapid test negative (i.e. false negative) samples was 31.92.ConclusionsThe sensitivity of the test in our symptomatic and asymptomatic cohort was lower than the manufacturer's reported sensitivity in a symptomatic cohort (97.1%). Despite their relatively lower sensitivity (especially in asymptomatic individuals), rapid tests have undeniable benefits (i.e., ease of use and rapid results) that make them a helpful tool in the control of the SARS-CoV-2 pandemic. Given the diagnostic accuracy of these tests as evidenced by this study, rapid tests can be thoughtfully employed in situations where swift results are critical.

Serum Total and Free Prostate Specific Antigen Levels as Novel Biomarker in Patients with COVID-19

Objectives: This study aimed to evaluate the diagnostic value of TPSA and FPSA for early detection of COVID-19 and compare it with a control group. Methods: This is a retrospective study of 146 patients from the Respiratory Clinic in Sebha city, 73 of whom have COVID-19 PCR-confirmed and 73 who do not have COVID-19 (group control). Results: The mean and standard division age in the PCR-confirmed COVID-19 group was 61.51±16.40. In the PCR-confirmed COVID-19 group, the mean and standard division serum biomarker level for TPSA was 0.51±.26 ng/ml, and 0.57± 0.32 for FPSA. Conclusion: For the biomarkers TPSA and FPSA, there were no significant differences between the control and PCR-confirmed COVID-19 groups. These findings suggest that the tumor biomarker may be ineffective in detecting COVID-19.

Experimental Technologies in the Diagnosis and Treatment of COVID-19 in Patients with Comorbidities.

The COVID-19 pandemic has impacted the whole world and raised concerns about its effects on different human organ systems. Early detection of COVID-19 may significantly increase the rate of survival; thus, it is critical that the disease is detected early. Emerging technologies have been used to prevent, diagnose, and manage COVID-19 among the populace in the USA and globally. Numerous studies have revealed the growing implementation of novel engineered systems during the intervention at various points of the disease’s pathogenesis, especially as it relates to comorbidities and complications related to cardiovascular and respiratory organ systems. In this review, we provide a succinct, but extensive, review of the pathogenesis of COVID-19, particularly as it relates to angiotensin-converting enzyme 2 (ACE2) as a viral entry point. This is followed by a comprehensive analysis of cardiovascular and respiratory comorbidities of COVID-19 and novel technologies that are used to diagnose and manage hospitalized patients. Continuous cardiorespiratory monitoring systems, novel machine learning algorithms for rapidly triaging patients, various imaging modalities, wearable immunosensors, hotspot tracking systems, and other emerging technologies are reviewed. COVID-19 effects on the immune system, associated inflammatory biomarkers, and innovative therapies are also assessed. Finally, with emphasis on the impact of wearable and non-wearable systems, this review highlights future technologies that could help diagnose, monitor, and mitigate disease progression. Technologies that account for an individual’s health conditions, comorbidities, and even socioeconomic factors can drastically reduce the high mortality seen among many COVID-19 patients, primarily via disease prevention, early detection, and pertinent management.

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies

COVID-19 Incidence and Mortality Among Long-Term Care Facility Residents and Staff in South Carolina

ObjectivesThis study explored differences in COVID-19 incidence, mortality, and timing among long-term care facility (LTCF) residents and staff with those living in the community in South Carolina (SC). DesignLongitudinal secondary data analysis. Setting and ParticipantsAdults age ≥18 in SC with confirmed COVID-19 diagnosis from 3/15/2020 and 1/2/2021 (n = 307,891). MethodsCOVID-19 data came from the SC Department of Health and Environmental Control (SCDHEC). We included all COVID-19 cases, hospitalizations, and deaths among adult residents. Residence and employment in LTCF were confirmed by SCDHEC. Descriptive statistics and trends for cases, hospitalizations, and deaths were calculated. We used Cox proportional hazards to compare COVID-19 mortality in LTCF residents and staff to community dwelling older adults and adults not employed in LTCF, respectively, controlling for age, gender, race, and pre-existing chronic health conditions. ResultsLTC residents experienced greater incidence of cases throughout the study period until the week ending on 1/2/21. LTCF residents with COVID-19 were more likely to be hospitalized compared to older adults in the community and 74% more likely to die (HR: 1.74, 95% CI: 1.59-1.90), after adjusting. LTC staff experienced greater incidence of cases compared to adults not employed in LTCF until the week ending on 12/26/2020, while experiencing similar incidence of death compared to the similar community members. After adjusting, LTC staff had 0.58 (HR = 0.58; CI: 0.39-0.88) times lower hazard of death compared to community members that did not work in a LTCF. Conclusions and ImplicationsNarrowing of the gap between LTCF and community-wide infection and mortality rates over the study period suggests that early detection of COVID-19 in LTCFs could serve as a first indicator of disease spread in the greater community. Results also indicate that policies and regulations addressing staff testing and protection may help to slow or prevent spread within facilities.

Review Paper on E-Smart Watch for Early Detection of Covid-19

Smart watches have the potential to support health in everyday living by: enabling self-monitoring of personal activity; obtaining feedback based on activity measures; allowing for in-situ surveys to identify patterns of behavior; and supporting bi-directional communication with health care providers and family members. However, smart watches are an emerging technology and research with these devices is at a nascent stage. Seventy-three studies were returned in the search. Seventeen studies published were included. Included studies were published from 2014 to 2016, with the exception of one published in 2011. Most studies employed the use of consumer-grade smart watches (14/17, 82%). Patient-related studies focused on activity monitoring, heart rate monitoring, speech therapy adherence, diabetes self-management, and detection of seizures, tremors, scratching, eating, and medication-taking behaviors. Most patient-related studies enrolled participants with few exclusion criteria to validate smart watch function (10/17, 58%). Smart watch technical function, acceptability, and effectiveness in supporting health must be validated in larger field studies that enroll actual participants living with the conditions these devices target.

A multi-layer model for the early detection of COVID-19.

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode—i.e. before the individual had the chance to report on any symptom—our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.