Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan · 6 authors
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.
Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.
With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.
Open access
COVID-19 diagnosis using AI
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Blockchain is an emerging technology based on a distributed digital ledger system. Decentralized trust is one of the key factors behind the blockchain-based system. The transparency of such a system is better than a conventional centralized ledger system. By using a blockchain-based transaction system, any business organization can harness key benefits like data integrity, confidentiality, and anonymity without involving any third party in control of the transactions. Since the blockchain is used in numerous applications, the horizon is expanding at an unprecedented pace. It was found that tracking COVID vaccination in a transparent and accountable way is an emerging need, especially after the pandemic outbreak around the world. The blockchain platform is a good match for such applications. In this study, a blockchain-based COVID-19 testing and vaccination tracking system, called COVAC, has been designed to manage the COVID testing and vaccination process for local organizations. The “Prototype Software Development" approach was used to determine the system requirements according to the practical knowledge obtained through the vaccine monitoring and screening tests process and then communicated with local healthcare facilities to determine whether these requirements were satisfied. The blockchain-based implementation ensured the system transparency, integrity, and security of data on COVID-19 testing and vaccination
Ana Côrte-Real, Tiago Nunes, Clara Cruz Santos, Paulo Rupino da Cunha
The increase of forcibly displaced people worldwide is a challenge for health systems and their ability to provide access and equity in Health as a universal right. Health information systems should be strengthened to collect and disseminate migrant health data enabling analytics for strategic decisions.This Viewpoint focuses on blockchain technology as an emergent digital tool to improve communication and overcome gaps in medical data sharing, conceptualizing a global health space. Anchored in the security, privacy, and medico-legal regulation of medical data, Blockchain technology would empower inter-organizational services or workflows, in real-time, by the users, inside and outside the national health systems, anywhere in the world. As an innovative approach, this Viewpoint highlights the future directions in IT-supported health.
Open access
Blockchain Technology Applications and Security
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Ioannis Skalidis, Olivier Müller, Stéphane Fournier
The recent pandemic launched an acceleration in adopting telemedicine by cardiovascular health and triggered the flourishing of technological advancements, such as the metaverse, which is a novel interactive mix of digital worlds that leverages augmented reality with virtual reality. The CardioVerse represents a theoretical term for the embracement of the metaverse by cardiovascular medicine, encompassing the endless possibilities as well as the challenges that it holds and introduces new dimensions to disease education, prevention and diagnosis. Its applications are numerous, notably in enhancing medical visits, assisting cardiovascular interventions and reshaping the way medical education is provided. Although obstacles are expected in diverse domains such as security, technical, legislative and regulatory, the utilization of non-fungible tokens as a security asset for patient data appears as potential solution.
Open access
Artificial Intelligence in Healthcare and Education
Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.
Subhi Alrubei, Edward A. Ball, Jonathan Rigelsford
In this study, a new blockchain protocol and a novel architecture that integrate the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, and blockchain were designed, developed, and validated. This new architecture has the ability to monitor the environment, collect data, analyze it, process it using an AI-expert engine, provide predictions and actionable outcomes, and finally share it on a public blockchain platform. For the use-case implementation, the pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used to test and evaluate the proposed system. Recently, various authors traced the spread of viruses in sewage water and studied how it can be used as a tracking system. Early warning notifications can allow governments and organizations to take appropriate actions at the earliest stages possible. The system was validated experimentally using 14 Raspberry Pis, and the results and analyses proved that the system is able to utilize low-cost and low-power flexible IoT hardware at the processing layer to detect COVID-19 and predict its spread using the AI engine, with an accuracy of 95%, and share the outcome over the blockchain platform. This is accomplished when the platform is secured by the honesty-based distributed proof of authority (HDPoA) and without any substantial impact on the devices’ power sources, as there was only a power consumption increase of 7% when the Raspberry Pi was used for blockchain mining and 14% when used to produce an AI prediction.
The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges.
Mohamed Torky, Essam Goda, Václav Snåšel, Aboul Ella Hassanien
The fight against the COVID-19 pandemic still involves many struggles and challenges. The greatest challenge that most governments are currently facing is the lack of a precise, accurate, and automated mechanism for detecting and tracking new COVID-19 cases. In response to this challenge, this study proposes the first blockchain-based system, called the COVID-19 contact tracing system (CCTS), to verify, track, and detect new cases of COVID-19. The proposed system consists of four integrated components: an infection verifier subsystem, a mass surveillance subsystem, a P2P mobile application, and a blockchain platform for managing all transactions between the three subsystem models. To investigate the performance of the proposed system, CCTS has been simulated and tested against a created dataset consisting of 300 confirmed cases and 2539 contacts. Based on the metrics of the confusion matrix (i.e., recall, precision, accuracy, and F1 Score), the detection evaluation results proved that the proposed blockchain-based system achieved an average of accuracy of 75.79% and a false discovery rate (FDR) of 0.004 in recognizing persons in contact with COVID-19 patients within two different areas of infection covered by GPS. Moreover, the simulation results also demonstrated the success of the proposed system in performing self-estimation of infection probabilities and sending and receiving infection alerts in P2P communications in crowds of people by users. The infection probability results have been calculated using the binomial distribution function technique. This result can be considered unique compared with other similar systems in the literature. The new system could support governments, health authorities, and citizens in making critical decisions regarding infection detection, prediction, tracking, and avoiding the COVID-19 outbreak. Moreover, the functionality of the proposed CCTS can be adapted to work against any other similar pandemics in the future.
Wei Yan Ng, Tien-En Tan, Prasanth V H Movva, Andrew Hao Sen Fang · 12 authors
The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.
V Akshita., Dhanush J. S, Dikshitha Varman. A, V. Krishna Kumar
Immunization is all about using Block chain for managing and tracing the vaccine stocks, logistics and transparent distribution. Immunization gives you continuous visibility and enables actionable insights to track vaccine distribution and ensure a fair and equitable distribution. Immunization allows you to book your vaccination appointments and will also allow you to keep track of the vaccine being distributed. Block chain helps in maintaining the integrity and transparency of the whole process right from inception of the vaccine.
Andy S. K. Cheng, Qiongyao Guan, Yan Su, Ping Zhou · 5 authors
This brief report aimed to describe a narrative review about the application of machine learning (ML) methods and Blockchain technology (BCT) in the healthcare field, and to illustrate the integration of these two technologies in cancer survivorship care. A total of six eligible papers were included in the narrative review. ML and BCT are two data-driven technologies, and there is rapidly growing interest in integrating them for clinical data management and analysis in healthcare. The findings of this report indicate that both technologies can integrate feasibly and effectively. In conclusion, this brief report provided the state-of-art evidence about the integration of the most promising technologies of ML and BCT in health field, and gave an example of how to apply these two most disruptive technologies in cancer survivorship care.
Open access
Artificial Intelligence in Healthcare and Education
Yazeed Alabdulkarim, Abdulmajeed Alameer, Mohammed Almukaynizi, Abdulaziz Almaslukh
The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform.
Shirajus Salekin Nabil, Md. Sabbir Alam Pran, Ali Haque, Narayan Ranjan Chakraborty · 6 authors
COVID-19 has changed almost all aspects of our lives. Governments around the world have imposed lockdowns to slow down the transmissions. Fortunately, we have found the vaccine, in fact, a good number of them. However, managing the testing and vaccination process of the total population is a mammoth job. However, there are always delays or data silo problems in multi-organizational work. Therefore, streamlining this process is vital to improve efficiency and save more lives. Because of its effective data sharing mechanism among different entities with a number of security features, blockchain can be an effective tool for different applications in the health sector. Furthermore, blockchain provides a distributed system along with greater privacy, transparency, and authenticity. In this article, we have presented a blockchain-based system that seamlessly integrates testing and vaccination systems, allowing the system to be transparent. The instant verification of any tamper-proof COVID-19 test result has been developed, which will serve as “Test Certificates”. A transparent and efficient vaccination system has also been exhibited and implemented as the “Digital Vaccine Passport” (DVP) system. The infection rate-based prioritization will ensure a transparent and fair vaccination process as well as tackle the distribution issue of the limited amount of vaccine. The comparative review with other existing works is also discussed, highlighting a clear difference from the existing works. Our proposed system is distinctive on the basis of prioritization of vaccines and seamless integration of test certificates and vaccine passports, which will aid in controlling the pandemic situation. This system will also be handy in the case of tackling any future pandemics initially.
Mohammed Baz, Sabita Khatri, Abdullah Baz, Hosam Alhakami · 6 authors
The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020, provide critical research criteria to assess the vulnerabilities of our current he... | Find, read and cite all the research you need on Tech Science Press
Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.
Sukhpal Singh Gill, Ricardo Vinuesa, Venki Balasubramanian, Soumya K. Ghosh
We are pleased to present a special issue that focuses on the software systems for managing the impact of the coronavirus disease 19 (COVID-19) pandemic. The COVID-19 pandemic has affected around 192 million people worldwide and has led to ˜4.13 million deaths as of July 22, 2021. Globally, most of the countries have implemented lockdowns to protect their citizens. However, lockdown over an extended period is unsustainable. Hence, it is widely believed that virus testing and tracking is the best approach to ease lockdown measures. There is a need for innovative software systems to manage the impact of the COVID-19 pandemic effectively in many areas such as healthcare system, transport systems, supply-chain system, educational system, government-service delivery, pharmaceutical companies, manufacturing, software industries, and multinational companies. For example, in healthcare, smart-software systems would be able to remotely measure a person's body temperature, heart and respiratory rates, identifying their movements (including sneezing, coughing, shivering, etc.) to identify whether a person is displaying symptoms of COVID-19 or not. An essential aspect associated with these technologies is data privacy, scalability, and quality of service (QoS) in terms of reliability, availability, security, latency, and energy which need to be considered throughout the development of the software systems. In countries like India, UK, Russia, Brazil, and USA, the system would also help to ensure that isolated communities have access to testing, delivered in a fast, accurate, and efficient manner. These software systems would help and support the assessment of public-health strategies and policies such as social distancing and assess further interventions to control the spread of the virus. Innovative software systems can increase stakeholder participation, as cost-effective assistance in the COVID-19 pandemic monitoring is of great interest to many countries. To manage the impact of this pandemic, there is a need to design and develop scalable, reliable, and energy-efficient sustainable software solutions for different COVID-19 scenarios. In consideration of the existing systems and their features, an Internet of Things (IoT)-based system suitable for COVID-19 or pandemic situations associated with other influenza viruses can be developed. Furthermore, these systems can be integrated with artificial-intelligence (AI) processes for effective data-collection, analysis, statistical visualization, sharing, and decision making. Moreover, these systems can be implemented using both simulations and real-time testbeds for COVID-19 operations (sanitization, medication, monitoring, thermal imaging, etc.) to test their performance in terms of scalability, reliability, availability, and energy efficiency. There is a need to use AI methods, such as reinforcement learning, deep learning, and genetic algorithms while developing IoT-based software systems to achieve self-learning, self-adaptation, and autonomous decision-making capabilities in order to improve efficiency of the systems. Meanwhile, a huge voluminous amount of complex data is generated from various sources including World Health Organization (WHO), social networking, edge devices, private and public hospitals, patients and academic institutes, which needs an effective big data analytics mechanism to manage this data proficiently. Furthermore, there is a need to study the impact of system configuration on workload processing at different cloud nodes while maintaining the QoS dynamically. The data are collected in databases, it is subsequently examined and monitored, and it is important to manage data consistency and integrity. In this context, we argue that it is essential to employ decentralized data-gathering approaches, maintaining the privacy of the population as a high priority. This special issue has received articles by researchers and practitioners from both academia and industry to develop innovative software systems for managing the impact of the COVID-19 pandemic. This special issue, therefore, aims to focus the attention of its readers to four research articles carefully selected after multiple rounds of peer-review. The brief contributions of these papers are discussed in the following section: The first paper entitled “An approach to forecast impact of COVID-19 using supervised machine learning model” by Mohan et al.1 proposes a hybrid model to predict the effect of COVID-19 using moving regressive, autoregressive, and ensemble learning model. This work uses two datasets from Worldometer and Ministry of Health & Family Welfare of India to conduct the countrywise predictions across the world and statewise predictions of India, respectively. The second paper entitled “NovidChain: Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates” by Abid et al.2 includes various promising ideas such as maintains the immutability and data integrity using Blockchain technology, enhances the privacy by incorporating encryption for personal information and verifies the COVID-19 proof using W3C verifiable credentials standard immediately. The third paper entitled “Software System to Predict the Infection in COVID-19 Patients using Deep Learning and Web of Things” by Singh et al.3 generates synthetic data using various data augmentation techniques. Proposed system uses U-Net and WoT to segment the COVID Medseg and Radiopedia datasets in an autonomic manner. Experimental results show that the system gives better performance in terms of network latency, response time, and server latency. The fourth paper entitled “Advanced Data Integration in Banking, Financial, and Insurance Software in the Age of COVID-19” by Maiti et al.4 contributes to recognize the effect of the COVID-19 pandemic on the global Banking Financial Services and Insurance landscape. Further, a hype cycle has been developed to find out the important software technologies to handle real-world challenges related to corporate. We believe the work that has been approved in this special issue will assist readers of the journal and a broader research community to learn about the topics of software systems and impacts of COVID-19 pandemic, and inspire them to study more in this area. We would like to express our gratitude to the Editor-in-Chief (Prof. Rajkumar Buyya) and editorial board members for allowing us to bring out this special issue and guiding us throughout the process. We also want to express our gratitude to and further acknowledge the administrative staff, reviewers, and especially the authors for their contributions to the success of this issue.
The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users' privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient's medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.
Open access
Blockchain Technology Applications and Security
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.