A Freehand 3D Ultrasound Carotid Scanning System for Point-of-Care Ultrasonography

Stroke is a leading cause of morbidity and mortality throughout the world. Three-dimensional ultrasound (3DUS) imaging was shown to be more sensitive to treatment effect and more accurate in stratifying stroke risk than two-dimensional ultrasound (2DUS) imaging. Point-of-care ultrasound screening (POCUS) is important for patients with limited mobility and at times when the patients have limited access to the ultrasound scanning room, such as in the COVID-19 era. We used an optical tracking system to track the 3D position and orientation of the 2DUS frames acquired by a commercial wireless ultrasound system and subsequently reconstructed a 3DUS image from these frames. The tracking requires spatial and temporal calibrations. Spatial calibration is required to determine the spatial relationship between the 2DUS machine and the tracking system. Spatial calibration was achieved by localizing the landmarks with known coordinates in a custom-designed Z-fiducial phantom in an 2DUS image. Temporal calibration is needed to synchronize the clock of the wireless ultrasound system and the optical tracking system so that position and orientation detected by the optical tracking system can be registered to the corresponding 2DUS frame. Temporal calibration was achieved by initiating the scanning by an abrupt motion that can be readily detected in both systems. This abrupt motion establishes a common reference time point, thereby synchronizing the clock in both systems. We demonstrated that the system can be used to visualize the three-dimensional structure of a carotid phantom. The error rate of the measurements is 2.3%. Upon in-vivo validation, this system will allow POCUS carotid scanning in clinical research and practices. © 2023 SPIE.

Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.

Enabling Society 5.0 through COVID-19 Digital Transformation, New Data Ecosystems, and Sustainability. Post-pandemic Legal Reflections*

The COVID-19 pandemic has accelerated the adoption of new technologies and raised societies' technological development. Legal regulations play an important role in the implementation of the latest technologies. The contact tracking applications that have been deployed almost all over the world do not, in most cases, provide an adequate level of personal data protection. An essential aspect of competition law regarding data protection is ensuring data security (trade secrets, personal information). Although entrepreneurs' use of cloud data stores is well-established, we have never witnessed adoption on such a large scale as during this pandemic. This was influenced by, among other factors, the need to provide remote access to enterprise resources despite limitations on the move-ment of people. Furthermore, because many companies faced an immediate need to enable remote working and collaboration, many solutions were adopted without the usual due diligence that should apply to such a business decision. Therefore, questions arise as to whether the legal frameworks for the functioning of such data ecosystems ensure their security. Moreover, since technological innovations are of key importance for sustainable development, it is worth reviewing the framing assumptions (concerning sustainable development) of active sustainability initia-tives and the possibilities of still achieving their goals despite the major setback of the pandemic. Furthermore, the rapid and forced changes resulting from the coronavirus pandemic cannot remain in place without adverse impact on the data protection landscape. During the pandemic, legal regulations regarding personal data protection, environmental protection, and competition law began to be questioned. The author shows that the legal regulations in force in these areas are no longer sufficient and require adaptation to the rapidly changing reality. © 2023, Spoldzielczy Instytut Naukowy. All rights reserved.

Meeting the Hospital Oxygen Demand with a Decentralized Autonomous PV System: Effect of PV Tracking Systems

When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.

Critical Commentary: Fickle spheres: The constant re/construction of the private and other new habits

The spread of the COVID-19 pandemic has allowed mechanisms of power and authority to enter new urban realms – especially the very relationships lived between friends and lovers in bedrooms and parks. All of a sudden, everyone has a right to know who we are close to, when and how, all for the sake of public health and safety, to ensure the further functioning of our established public health system. The new policies transform Western ideas of public and private spheres: our bedrooms have turned into the space of self-representation and workplaces at the same time. On the other hand, what had been known as public space before has turned into the space to be private in: a walk through the city alone or with an intimate person. Yet all of these tendencies come with increased surveillance, not only by our peers, but also through technologies such as tracing apps. The very possibility of privacy and 'active' publicity is being questioned, and, through this, the realm of the political. This paper traces the observed shifts in the nature of the private and public spheres through examples in German cities, tracing power via embodied experiences. Those traces are reorganised into three argumentative strands: re/constructing privacies, public space as non-place and the proliferation of the data body. Based on these observations the paper searches for emancipatory perspectives within the shifted spheres of urban social life. (English) [ FROM AUTHOR] 新冠疫情的蔓延使权力和权威机制进入了新的城市领域—尤其是朋友和恋人们之间在卧室和公园里的关系。突然之间，每个人都有权知道我们与谁、何时以及如何亲密接触，这一切都是以公共卫生和安全的名义，为了确保我们既定的公共卫生系统的进一步运作。新政策改变了西方对公共和私人领域的看法：我们的卧室同时变成了自我展示的空间和工作场所。另一方面，以前被称为公共空间的地方已经变成了私密的空间：独自或与亲密的人一起在城市中漫步。然而，所有这些趋势都伴随着越来越多的监控，不仅来自我们身边的人，还通过追踪应用程序等技术。隐私和"主动"曝光的可能性，进而政治领域正受到质疑。本文通过德国城市的例子（通过具身体验追踪权力）追踪观察到的私人和公共领域性质的变化。这些追踪被重组为三股争论：重新/构建隐私、作为非场所的公共空间和数据体的扩散。基于这些观察，本文在城市社会生活的变化范围内寻找解放性的视角。 (Chinese) [ FROM AUTHOR] Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Self-Tracking to Do Less: An Autoethnography of Long COVID That Informs the Design of Pacing Technologies

Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these "pacing technologies"should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life. © 2023 Owner/Author.

Real-Time and Accurate Monitoring of Social Distance and Contact Tracing during COVID-19 Situation

In recent years, a lot of research works have been done on object detection using various machine learning models. However, not many works have been done on detecting and tracking humans in particular. This study works with the YOLOv4 object detector to detect humans to use the detections for maintaining social distance. For this study, the YOLOv4 model is trained on only one class named 'Person'. This is done to improve the speed of detecting humans in real time scenario with satisfying accuracy of 97% to 99%. These detections are then tracked to build a system for maintaining social distance and alerting the authority if a breach in the social distance is detected. This system can be applied at ticket counters, hospitals, offices, factories etc. It can also be used for maintaining social distance among the students and the teachers in the classroom for their safety. © 2022 IEEE.

A Low-Cost Wireless Body Area Network for Human Activity Recognition in Healthy Life and Medical Applications

Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios. Author

Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

The Ethics of Location Tracking During the COVID-19 Pandemic and Beyond: Building Awareness and Consensus

This article highlights the relatively limited but growing discussion surrounding ethical guidelines for the use of location tracking technology. After a review of recent literature related to location data and geoprivacy, this article is divided into two sections: The first highlights views of public officials and location tracking experts over the potential misuse of location data, especially in the context of the COVID-19 pandemic. The data come from available transcripts of the Location Tech Task Force organized in 2020 by the American Geographical Society as part of its EthicalGEO initiative. The second section documents various institutional approaches to elevate the dialogue and inform governance of location-based data and technology, including the development of the Locus Charter, an emerging international framework on the ethical use of location data. In conclusion, we urge the professional and academic geographic communities to engage with the elaboration and dissemination of ethical frameworks to guide the use and management of data from location tracking technology. (English) [ FROM AUTHOR] La reciente erudición geográfica feminista ha urgido a los geógrafos a distanciarse de los enfoques androcéntricos y eurocéntricos, y a abrir la disciplina a perspectivas diversas. En tanto que numerosos estudios se han enfocado a diversificar y descolonizar la geografía por medio de prácticas de reclutamiento, tutoría y producción de conocimiento, solo muy pocos han analizado cómo se traduce la diversidad en las prácticas de enseñanza, en particular en contextos donde la diversidad está relativamente bien establecida entre el personal. Basado en una encuesta por cuestionario entre el personal docente, en un análisis del contenido de los programas de los cursos y un análisis cuantitativo de los datos de los empleados del departamento, este artículo explora hasta qué punto la diversidad dentro del departamento conduce a la diversidad en las prácticas de la enseñanza. Desarrollando un marco de los espacios de la diversidad, analizamos tres espacios que potencialmente permiten practicar la diversidad en la enseñanza: El espacio académico del departamento promueve la libre elección de los tópicos de investigación y enseñanza, y las condiciones flexibles del trabajo;el espacio del departamento permite a los individuos asumir compromisos en la configuración de la enseñanza geográfica;y el espacio del conocimiento promueve la diversidad como un ideal. Sin embargo, encontramos que practicar la diversidad en geografía implica enfrentar los retos de las estructuras universitarias tradicionales y neoliberales y de las jerarquías formales y percibidas. Aún más, existe una necesidad de prácticas concretas sobre diversidad a niveles individuales e institucionales para llevar activamente las diversas perspectivas al salón de clase. (Spanish) [ FROM AUTHOR] 女权地理学的最新研究, 敦促地理学者远离以男性和欧洲为核心的方法, 接受不同的观点。许多研究都侧重通过招聘、指导和知识生产, 去实现地理学的多样化和去殖民化。只有少数研究分析了多样性如何转化为教学实践（尤其是在教职员工多样性相对稳定的情况下）。基于教师问卷调查、课程大纲内容分析以及对地理系员工数据的定量分析, 本文探讨了地理系的多样性在多大程度上导致教学实践的多样性。我们建立了一个多样性的空间框架, 分析了可能实现教学多样性的三个空间："学术空间"促进对研究课题、课程题目和灵活工作条件的自由选择, "地理系空间"使个人能够参与地理教学的建设, "知识空间"促进理想的多样性。然而, 传统的和新自由主义的大学体系以及严格的等级制度, 是实现地理多样性的挑战。此外, 还需要在个人和体制层面采取切实的多样性实践, 积极地将不同观点带入课堂。 (Chinese) [ FROM AUTHOR] Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Close Contact Individual Monitoring Based on Prescribed Social Distance Using Computer Vision

Contact tracing is one of the methods used by the government and organizations for controlling viral diseases like COVID-19, which claimed many human lives. Social distancing is advised to everyone to minimize the virus from spreading. This study aims to build a contact tracing tool that monitors social distancing individually using computer vision in real-time. Object tracking by detection is used for individual monitoring with YOLOv4 (You Only Look Once) as the object detector and SORT (Simple Online and Real-time Tracking) as the object tracker. The combination gained an average streaming and detection frame rate of 26 FPS and 10 FPS on NVIDIA's GTX 1650, respectively. It is expected to have more frame rate when used in a more powerful device. Moreover, the system obtained 98.2% accuracy in measuring the distance between individuals. Furthermore, the performance of the QR scanner used in the study attains a 100% success rate and a 98% accuracy in allocating the QR code to the correct owner from the video stream. © 2022 IEEE.

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

Left Ventricular Global Longitudinal Strain as a Parameter of Mild Myocardial Dysfunction in Athletes after COVID-19.

Whether symptoms during COVID-19 contribute to impaired left ventricular (LV) function remains unclear. We determine LV global longitudinal strain (GLS) between athletes with a positive COVID-19 test (PCAt) and healthy control athletes (CON) and relate it to symptoms during COVID-19. GLS is determined in four-, two-, and three-chamber views and assessed offline by a blinded investigator in 88 PCAt (35% women) (training at least three times per week/>20 MET) and 52 CONs from the national or state squad (38% women) at a median of two months after COVID-19. The results show that the GLS is significantly lower (GLS -18.53 ± 1.94% vs. -19.94 ± 1.42%, p < 0.001) and diastolic function significantly reduces (E/A 1.54 ± 0.52 vs. 1.66 ± 0.43, p = 0.020; E/E'l 5.74 ± 1.74 vs. 5.22 ± 1.36, p = 0.024) in PCAt. There is no association between GLS and symptoms like resting or exertional dyspnea, palpitations, chest pain or increased resting heart rate. However, there is a trend toward a lower GLS in PCAt with subjectively perceived performance limitation (p =0.054). A significantly lower GLS and diastolic function in PCAt compared with healthy peers may indicate mild myocardial dysfunction after COVID-19. However, the changes are within the normal range, so that clinical relevance is questionable. Further studies on the effect of lower GLS on performance parameters are necessary.

Right atrial reservoir strain and right ventricular strain improves in patients recovered from hospitalisation for non-severe COVID-19.

PURPOSE: Those hospitalised with coronavirus disease 2019 (COVID-19) have recently been shown to have impaired right ventricular (RV) strain, but data about the course of heart function after discharge are limited. Our aim was to compare right ventricular strain and right atrial reservoir strain (RASr) associated with COVID-19 between acute disease (during hospitalisation) and follow-up (after discharge). METHODS: In this retrospective single-center study, we analysed the echocardiograms of 43 patients hospitalised for non-severe COVID-19 between December 2020 and March 2021, undergoing echocardiography both during and after hospitalisation. In addition to conventional echocardiographic parameters, we applied 2-dimensional speckle tracking to obtain RV global longitudinal strain (RV-GLS), RV free wall strain (RV-FWS), and RASr. RESULTS: Mean (standard deviation) age of the study population was 50 (9) years, and 18 (42%) of the participants were women. Median duration between exams was 6 months (range, 5-7 months). Both mean RV-GLS and mean RV-FWS significantly increased at follow-up (-20.8 [3.8] vs. -23.5 [2.8], p < 0.001 and -23.3 [4.2] vs. -28.2 [2.8], p < 0.001; respectively), and RASr significantly improved as well (-32.3 [6.6] vs. -41.9 [9.8], p < 0.001). CONCLUSION: In patients hospitalised for non-severe COVID-19 pneumonia, RV-GLS, RV-FWS, and RASr improved significantly between acute disease and 6 months after discharge.

COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.

Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.

Does wearing facial masks increase perceived facial attractiveness? An eye-tracking experiment.

As wearing a mask has become a routine of daily life since COVID-19, there is a growing need for psycho-physiological research to examine whether and how mask-fishing effects can occur and operate. Building upon a notion that people are likely to utilize information available from the facial areas uncovered by a mask to form the first impression about others, we posit a curvilinear relationship between the amount of the facial areas covered by a mask and the perception of others' attractiveness such that the attractiveness perception increases initially and then decreases as more facial areas are covered by a mask. To better examine this covering effect, we conduct an experiment using an eye-tracker and also administer a follow-up survey on the facial attractiveness of target persons. Our results showed that the facial attractiveness of target persons increased as the areas covered by a mask increased as in the moderate covering condition where the target persons wore only a facial mask, demonstrating that the mask-fishing was indeed possible thanks to the covering effect of a mask on the facial attractiveness. The experimental results, however, revealed that the mask-fishing effect disappeared as the areas covered increased further as in the excessive covering condition where the target persons' face and forehead were covered with a mask and a bucket hat. More importantly, the eye-tracking data analysis demonstrated that both the number of gaze fixation and revisits per unit area were significantly lower in the moderate covering than in the excessive covering condition, suggesting that participants in the moderate covering were able to form the impression about the target persons using cues available from the eyes and forehead areas such as hairstyle and eye color whereas those in the excessive covering were provided only a limited set of cues concentrated in the eyes area. As a result, the covering effect no longer existed under the excessive covering. Furthermore, our results showed that participants in the moderate covering were more likely than those in the excessive condition to exhibit the higher level of curiosity and perception of beautifulness but perceived the lower level of coldness when evaluating the target persons. The current research offers theoretical contributions and practical implications made from the eye-tracking experiment and discusses possible avenues for further research.

Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking.

The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.

High-throughput microbead assay system with a portable, cost-effective Wi-Fi imaging module, and disposable multi-layered microfluidic cartridges for virus and microparticle detection, and tracking.

In recent years biomedical scientific community has been working towards the development of high-throughput devices that allow a reliable, rapid and parallel detection of several strains of virus or microparticles simultaneously. One of the complexities of this problem lies on the rapid prototyping of new devices and wireless rapid detection of small particles and virus alike. By reducing the complexity of microfluidics microfabrication and using economic materials along with makerspace tools (Kundu et al. 2018) it is possible to provide an affordable solution to both the problems of high-throughput devices and detection technologies. We present the development of a wireless, standalone device and disposable microfluidics chips that rapidly generate parallel readouts for selected, possible virus variants from a nasal or saliva sample, based on motorized and non-motorized microbeads detection, and imaging processing of the motion tracks of these beads in micrometers. Microbeads and SARS-CoV-2 COVID-19 Delta variant were tested as proof-of-concept for testing the microfluidic cartridges and wireless imaging module. The Microbead Assay (MA) system kit consists of a Wi-Fi readout module, a microfluidic chip, and a sample collection/processing sub-system. Here, we focus on the fabrication and characterization of the microfluidic chip to multiplex various micrometer-sized beads for economic, disposable, and simultaneous detection of up to six different viruses, microparticles or variants in a single test, and data collection using a commercially available, Wi-Fi-capable, and camera integrated device (Fig. 1).

Cooperative Attention-Based Learning between Diverse Data Sources

Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of 67% and can dynamically optimize the decision model using a control algorithm with a complexity of order O(n(2)).