Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.

Data Sharing and Information Platforms in Crisis Response and Preparedness: Exploring the Role of Open Data Sharing Platforms and Collective Intelligence in COVID-19 Response Efforts, and Preparedness for Future Pandemics

COVID-19 has wreaked unprecedented havoc in the world. Response efforts have also made huge evident gaps in preparedness and governments around the world's capacity to respond to a health crisis of this magnitude adequately. As a result, local communities have taken matters into their own hands and turned to technology platforms to coordinate mutual aid efforts, shed light on response gaps, and hold governments accountable. This paper explores the role of open data sharing platforms and collective intelligence in COVID-19 response efforts by studying two examples of community-led initiatives from Spain and Japan. Frena La Curva (Spain) and Safecast (Japan) utilized the Ushahidi platform, an open-source technology tool born out of Kenya's post-election violence that has been widely used in over 160 countries for crisis response since its inception in 2008. Research reports have been warning of pandemic breakouts for decades. However, the response to COVID-19 was inadequate, with healthcare systems buckling under the pressure of the spread of the disease. Moreover, existing social protection programs could not shield citizens despite having experienced similar economic impacts in the years that have passed. Data hugging and suppression of information regarding the pandemic outbreak led to significant delays in measures being put in place to curb the spread of COVID-19. This paper proposes that governments would benefit from leveraging open data and technology platforms to engage with ordinary citizens and eliminate data blind spots in the design of social protection programs. It also posits that we need to invest in interoperable data exchange systems to increase the speed of response and learning. Finally, it also proposes the need for internet freedom and access as a critical tool for preparedness by enabling the free flow of information. © 2022 Copyright held by the owner/author(s).

Collaborative Design in Concurrent Engineering of Industry 4.0 Through Virtual Reality Simulation in Achieving Accelerated Time-To-Market During COVID-19 (Coronavirus) Pandemic Outbreak

In the era of industry 4.0, it is important for businesses to conduct design and engineering activities via data exchange in cloud computing. Collaborative design in concurrent engineering through virtual reality simulation using digital mock up (DMU) presented in this paper is to construct a simulation in digital environment in order to reduce development time, cost and improve quality of product which will impact to accelerated time to market. However, due to current COVID-19 (Coronavirus) pandemic outbreak, the activities could be interrupted. The virtual reality design collaboration seems to be as a solution with the support of cloud storage of product design and satisfy design issue in concurrent engineering. It is also overcome the geographical limitation and social distancing constraint in “face to face” collaboration. Thus, stakeholders’ activities are carried out as usual without interruption in the challenging period during the pandemic. Finally, virtual simulation of collaborative design in concurrent engineering of previous related works conducted by the author are briefly presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 Symptoms Detection System for Campus Access using WSN

The Covid-19 pandemic caused a lot of dramatic changes to the international education system. Regardless of the advantages of the online-based education system, the conventional on-campus education system has a lot of benefits that cannot be ignored, especially for lab-based courses. The main goal of this proposed paper is to either mitigate or eliminate the hazards of the Covid-19 virus's spreading between students and staff, so they can attend on-campus events and activities safely. In this paper, we proposed an Arduino-based solution to automate the detection of the Covid-19 symptoms of students and staff within a campus. The proposed solution consists of two separate sub-systems. The first sub-system is the Covid-19 Symptoms Detection System (Covid-19 SDS), which is responsible for detecting Covid-19 symptoms by measuring the temperature and heart rate. The second sub-system is the Campus Authenticator Sign-on System (CASS), which is responsible for checking whether the person is authorized to have access or not to the campus. We used Wireless Sensor Network (WSN) and Near Field Communication (NFC) for the data exchange between the nodes. The experiments showed an acceptable reading precision with an error margin of around ± 1.35. © 2022 IEEE.

Inspection Data Exchange and Visualization for Building Maintenance using AR-enabled BIM

After the COVID-19 outbreak, a new concept of building maintenance (BM) systems is needed because current approaches highly rely on physical contact between workers, engineers, and managers. It imposes health and safety risks as increasing concerns about infections and spreads. This adds burdens to take unavoidable close contact and health risks to building owners, occupants, workforce, and society at large. In this respect, a new BM system was developed that enables reliable virtual communication and reduces BM response times by filling gaps between users and building managers. The proposed system is based on a concept of a cyber-physical system (CPS) using augmented reality (AR) and building information modeling (BIM) to promote non-contact building management. In this system, AR plays an important role in inspecting and visualizing defects in the real world, and the detected defect information is stored and managed by cloud-based BIM in cyberspace. This paper focuses on data visualization and management in the CPS-based non-contact building management system. A cloud-based database and mobile application are developed for data management purposes. In addition, this paper presents BIM data exchange and visualization in AR applications. Target image-based localization and tracking in BIM are also tested. The test results showed that the model alignment and localization accuracy are reliable for building maintenance works. Using the new BM mechanism, we expect that the related workers, building owners, and occupants will experience a reliable building maintenance process based on CPS-based information exchange from both users and facility managers while maintaining social distance. © 2022 International Association on Automation and Robotics in Construction.

A Remote Laboratory for Teaching Internet of Things in Online Education

Like almost in every field, application and design of IoT is also important in mechanical enginering for construction of automated machines, production lines and process plants. Education of engineers has to follow this trend. A new course on this field was established and had to be organised online according to Covid pandemic restrictions. The challenge to enable online programming exercises on an embedded system, where students at home control some process hardware over the internet, had to be solved. In this work, a solution is presented. Three communication paths were used, one was an office communication platform for creating a virtual classroom. Furthermore a server providing command line interfaces for communication of the programmers with the target hardware was established. Finally, an external MQTT broker was used for data exchange of the controller hardware with an application running on the PC. A low-cost hardware was contructed to provide a realistic thermal process. About twenty students completed the course with good results. © 2022 IEEE.

Remote Health Monitoring and Doctor on Call System

People's lives can be lost if they do not receive timely medical treatment;therefore, prompt medical care is vital. Furthermore, due to a lack of constant vital monitoring, early symptoms of major medical conditions, such as an irregular heartbeat or abnormal ECG output, are occasionally ignored. As a result, remote monitoring of the elderly and disabled is important during the COVID 19 outbreak. To solve these issues, a "Remote Health Monitoring and Doctor on Call System"has been developed. Health data exchange between healthcare providers and family members is becoming more prevalent these days. It ensures that patients' health results are safer and better. Sharing health-care data is also essential for lowering health-care expenses. It is feasible to remotely monitor a crippled or elderly patient's health. In addition to specialists, guardians can obtain a comprehensive picture of the patient's medical history. To address the issue of insufficient medical care and assistance for elderly and disabled patients, a unique and comprehensive Remote Health Monitoring and Doctor on Call System that can monitor the patient's vital signs such as heart rate, body temperature, and ECG output of the heart;monitor the patient's environment;display and store all of this information via a cloud server using ThingSpeak and Ubidots IoT platform;display these critical statistics with a mobile application for Android/iOS and in the event of an emergency or if vital signs are abnormal, contact the nearest hospital was developed in this paper. © 2022 IEEE.

Pivoting Online: The Case of the Agri-Food Sector

The debate concerning e-Commerce adoption is an evolving one that in the context of the current COVID-19 Pandemic has come centre stage. Although the shift towards e-Commerce platforms has been an increasing trend in recent years, Curtin (2020) states the current pandemic has accelerated consumers' shifts toward e-Commerce by five years. As store closures and new enforcements became a reality, it forced many companies to pivot their online strategies overnight (Shadler et al, 2020). Since its inception in the late 90's, e-Commerce adoption and research has championed the rise and the significant benefits of e-Commerce. Industries as diverse as financial, retail, manufacturing, and hospitality (Josanov, 2011;Kremez et al, 2019;Helper and MacDuffie, 2000;Hua, 2016) have embraced e-Commerce. By contrast, research into the use and level of adoption by Agri-food businesses indicates that this sector has not kept pace with this digital revolution. This is surprising given the importance of this sector worldwide. Indeed, it is one of the most important indigenous industries in Ireland in terms of employment, economic output, and export performance. However, there is scarce research found in the case for Irish Agri-food e-Commerce development, with limited industry reports available. Previous research elsewhere called for government support to encourage the move to online territory (Sparkes and Thomas, 2001;Sturiale and Scuderi, 2016), and the onset of COVID-19 furthered this. Numerous support agencies expanded on their current offerings in relation to digital support, although little is known around Agri-food businesses adoption of these. For this reason, this paper intends to add to the limited studies surrounding this important indigenous industry in Ireland within the widely dynamic topic of eCommerce platforms. The focus of this paper is directed toward the adoption and integration of these online platforms during COVID-19, the benefits and challenges faced, the role of support agencies and further support suggested by SMEs through survey data. This study has both an academic and industry focus and hence aims to improve our understanding of online activity and digital support within this sector.

A Review on Impact of COVID-19 on E-Commerce

With technology advancing at such a substantial pace, we have entered into an era where even business processes are taking place over the internet. It has also become the need of an hour because at present the entire world is dealing with a pandemic which has left everybody to stay back at their homes, even meeting our friends and relatives or going outside for essentials have become strenuous. For reducing this psychological stress and providing comfort to people e-commerce has played a major role in every aspect, people during the pandemic have used electronic commerce in ordering food to essentials. The paper contains a study and review of various research papers and articles on how the COVID-19 pandemic has impacted on electronic commerce industry. The paper also presents the contribution of e-commerce in the current situation how it has helped people across the world by delivering essentials and other goods and services. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Importance of Data Quality to Reinforce COVID-19 Vaccination Scheduling System: Study Case of Jakarta, Indonesia

Vaccination is one of the solutions to reduce the spread of COVID-19. Jakarta Government collaborates with the Department of Population and Civil Registration and the Provincial Health Office to build a COVID-19 vaccination scheduling system to reinforce the vaccination process in Jakarta. The development process involves 3 major stakeholders, so it requires very intense coordination and data exchange. Department of population and civil registration provides population data as vaccination targets. This data has been integrated into the system of the Jakarta Government. However, some other data, such as the location and quota of vaccination from the Provincial Health Office is collected manually using a spreadsheet. Manual exchanging data tends to cause data is often inaccurate, incomplete, inconsistent, and duplicate. This study aims to measure data quality (DQ) of the COVID-19 vaccination scheduling system in Jakarta. This study uses Total Data Quality Management (TDQM). TDQM provides a common framework to facilitate understanding in data improvisation through data quality management. Measurement and analysis of the data on database of the system using a tool, Talend. The measurement discovers that completeness (null 60.80% and blank 21.36%), validity 92.18%, accuracy 99.11%, and uniqueness 99.38%. The result shows that some data were poor quality especially due to incomplete data. © 2022 IEEE.

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Cluster-based hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs. The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy (LEACH), which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation. This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process, thus increasing the network lifespan. This paper proposes the LEACH-CHIO, a centralized cluster-based energy-aware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO) algorithm. CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process. LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios, which consist of a variable number of nodes ranging from 20 to 100. To evaluate the algorithm performances, three evaluation indicators have been examined, namely, power consumption, number of live nodes, and number of incoming packets. The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators. © 2022 Tech Science Press. All rights reserved.

Detecting and Preventing Faked Mixed Reality

Virtualized collaboration can significantly increase remote management of critical infrastructures. Crises such as the current COVID-19 pandemic push the technology: they require remote management to keep our infrastructures running. Mixed Reality (MR) prototypes enable remote management in diverse fields such as medicine, industry 4.0, energy systems, education, or cyber awareness. However, the evolution of virtualized collaboration is still in the beginning. By design, MR is fake: its reality is generated from models. This makes detecting attacks very difficult. Many MR-attacks result from well-known cybersecurity threats. This paper identifies classic attack surfaces, vectors, and concrete threats that are relevant for MR. It presents mitigation methods that can help to secure the underlying data exchanges. However, distributed systems are often heterogeneous and under different management authorities, making securing the entire virtualized remote management stack difficult. The paper therefore also introduces considerations towards an MR-client-based attack detection, i.e., MR-forensics, including relevant features and the use of machine learning. © 2021 IEEE.

Architecture Design of AI and IoT based System to Identify COVID-19 Protocol Violators in Public Places

The World Health Organization (WHO) describes COVID-19 as a pandemic that is causing a worldwide health disaster. Wearing a face mask in public places is the most effective method to curb the spread of the virus. The Internet of Things is emerging as one of the most significant innovations and playing a vital role during the pandemic. Affordable remote health monitoring devices help doctors to track quarantined patients. Our government is trying its best to control the spread of the virus. Citizens who do not follow the protocols serve as the reason for these widespread infections. Our work proposes a system to identify protocol violators in real-time. Our system consists of a face mask detection module, a social distance monitoring module, and a non-contact temperature monitoring module. We intend to deploy our proposed approach in public places such as airports, schools, and hospitals. These modules depend on video feeds from general security cameras and IoT sensor nodes deployed around public areas. Health care and security officials can subscribe to real-time data feeds to track public behaviour. © 2021 IEEE.

Trusted Compliance Enforcement Framework for Sharing Health Big Data

COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations. © 2021 IEEE.

Growing Process of Communities on Data Platforms: Case Analysis of a COVID-19 Dataset

In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value. © 2021 IEEE.

Enhancing Supply Chain Capabilities with Blockchain Deployment: An RBV Perspective

Blockchain has been widely acknowledged as a disruptive technology to improve supply chain performance. For achieving operational efficiency and quality excellence, organizations need the underlying supply chain capabilities. Supply chain capability is considered a higher-level capability to reconfigure various capabilities to achieve excellence in supply chain operations. Blockchain deployment in supply chains enhances operational and strategic capabilities that ultimately lead to better supply chain performance. The study investigates the blockchain from Resource-Based View (RBV) perspectives as an enabler of various capabilities that disrupt supply chain operations. RBV theory explains how a firm can achieve a competitive advantage by transforming its unique resources into capabilities. The prime focus of the study is to demonstrate the capabilities and benefits of blockchain deployment in the supply chain based on RBV perspectives and provide various frameworks to emphasize supply chain transformations on blockchain deployment.

Using FIWARE and Blockchain in Post Pandemic Vaccination Scenario

The emergence of the COVID-19 pandemic, caused by the SARS-Cov-2 virus, directly affected the displacement of people around the world. Some countries have restricted travel from certain countries where the pandemic has not been controlled. It is expected that with the emergence of different types of vaccines to contain the spread of this virus, international travel will resume both for business, tourism, and vacation. However, how to guarantee and verify that someone was vaccinated? Therefore, it is necessary to integrate most vaccination systems worldwide to offer digital proof of vaccination. One way to do this is to use FIWARE, a curated framework from an open-source platform for smart solutions, which utilizes NGSI Standards, enabling the integration of components and providing the basis for the interoperability and portability of smart solutions. However, data exchanged between systems need to be reliable and secure. One way is to overcome these challenges is using Blockchain with NGSI as an immutable decentralized transaction ledger to integrate systems. This paper proposes a new Generic Enabler (GE) named Canis Major to be integrated into the FIWARE ecosystem solutions to provide secure interoperability between systems used to integrate vaccination systems worldwide in a post-pandemic vaccination scenario. With this solution, any integrated vaccination system can reliably verify when and what type of vaccine a person has been vaccinated to contribute to the end of the pandemic. © 2021 IEEE.

Editor's Note

The AACRAO Speede Committee shares a look back, reviews current practices, and predicts the future of standardized data formats and representations of student learning like the Comprehensive Learner Record (CLR). Tana Miller distills the essence of a recent American Council on Education (ACE) report on international students and enrollment. According to Miller, this is a must read for anyone who is interested in maintaining or growing international student enrollment.