Blockchain and COVID-19 pandemic: applications and challenges.

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

COVID-19, jobs and skills-Exploratory analysis of the job postings in the US and UK healthcare job market.

The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner.

Utilization of Antibiotics for Hospitalized Patients with Severe Coronavirus Disease 2019 in Al-Madinah Al-Munawara, Saudi Arabia: A Retrospective Study.

Background: Most patients admitted to intensive care units (ICUs) with severe Corona Virus Disease 2019 (COVID-19) pneumonia receive antibacterial antibiotics with little evidence of bacterial infections. Objective: This study was designed to review the profiles of patients with severe COVID-19 pneumonia requiring intensive care, the rate of bacterial coinfection, the antibiotics used, and their relation to patient outcomes (death or recovery). Methods: This was a retrospective study that reviewed the medical records of all patients with confirmed COVID-19 (n = 120) severe pneumonia admitted directly from the emergency room to the intensive care unit, at a public hospital during the period from May 2020 to April 2021. The data collected included patients' demographic and laboratory data, comorbidities, antibiotic treatment, and their outcome. Descriptive statistics, bivariate inferential analysis tests (chi-square and unpaired T-Tests) and multivariable binary logistic regression were performed. Results: The mean age of the patients was 56.8 ± 16.5 years old, and among them, 74 (62.7%) were males. Of the included patients, 92 (77.0%) had comorbidities, 76 (63.3%) required mechanical ventilation and 30 (25%) died. All patients received empirical antibiotics for suspected bacterial coinfection. The most common antibiotics used were azithromycin (n = 97, 8%) and imipenem (n = 83, 9%). Ninety patients (75%) were on two empirical antibiotics. Early positive cultures for pathogens were found only in four patients (3.3%), whereas 36 (30%) patients had positive cultures 5-10 days after admission. The most frequently isolated pathogens were Acinetobacter baumannii (n = 16) and coagulase-negative Staphylococci (n = 14). In bivariate analysis empirical treatment with azithromycin resulted in a significantly lower mortality rate (p = 0.023), meanwhile mechanical ventilation, days of stay in intensive care unit, morbidities (e.g., lung disease), linezolid and, vancomycin use associated with mortality (p< 0.05). The adjusted logistic regression, controlling for age and gender, revealed that azithromycin antibiotic was more likely protective from mortality (OR= 0.22, 95%CI 0.06-0.85, p=0.028. However, patients with lung diseases and under mechanical ventilation were 35.21 and 19.57 more likely to die (95%CI =2.84-436.70, p=0.006; 95%CI=2.66-143.85, p=0.003, respectively). Conclusion: Bacterial coinfection with severe COVID-19 pneumonia requiring intensive care was unlikely. The benefit of Azithromycin over other antibiotics could be attributed to its anti-inflammatory properties rather than its antibacterial effect.

The Cloud Computing Systems' Adoption in the Higher Education Sector in Oman in Light of the COVID-19 Pandemic

The objective of the research was to find out the key factors that influence the acceptance and usage of cloud computing systems in the Omani higher education sector, with special emphasis on the COVID-19 outbreak. For this purpose, a quantitative research approach was conducted where 200 students from several Omani higher education institutions were surveyed, and by using Partial Least Square (PLS) to analysis the collected data. The findings revealed that the intention to use cloud computing in this context is significantly dependent on its perceived ease of use, usefulness, perceived reliability and responsiveness. It is noteworthy that this is one of the early studies that address the subject of cloud computing usage during times of crises, and specifically the COVID-19 outbreak. As such, it provides significant contributions in the area of technology adoption.

Blockchain-Based Decentralized Digital Manufacturing and Supply for COVID-19 Medical Devices and Supplies.

Coronavirus 2019 (COVID-19) has disclosed the deficiencies and limitations of the existing manufacturing and supply chain systems used for medical devices and supplies. It enforces the necessity to accelerate the shift towards decentralized digital manufacturing and supply chain networks. This paper proposes a blockchain-based solution for decentralized digital manufacturing of medical devices and their supply. We develop Ethereum smart contracts to govern and track transactions in a decentralized, transparent, traceable, auditable, trustworthy, and secure manner. This allows overcoming certain issues hindering the transition towards decentralized digital manufacturing and supply, including trusted traceability, attestations, certifications, and secured intellectual property (IP) rights. We incorporate the decentralized storage of the InterPlanetary file system (IPFS) into the Ethereum blockchain to store and fetch Internet of things (IoT)-based devices records and additional manufacturing and supply details. We present the system architecture and algorithms along with their full implementation and testing details. Furthermore, we present cost and security analyses to show that the proposed solution is cost-efficient and resilient against well-known vulnerabilities and security attacks. We make our smart contracts code publicly available on GitHub.

Effect of Environmental Pollutants PM2.5, CO, NO2, and O3 on the Incidence and Mortality of SARS-CoV-2 Infection in Five Regions of the USA.

In recent decades, environmental pollution has become a significant international public problem in developing and developed nations. Various regions of the USA are experiencing illnesses related to environmental pollution. This study aims to investigate the association of four environmental pollutants, including particulate matter (PM2.5), carbon monoxide (CO), Nitrogen dioxide (NO2), and Ozone (O3), with daily cases and deaths resulting from SARS-CoV-2 infection in five regions of the USA, Los Angeles, New Mexico, New York, Ohio, and Florida. The daily basis concentrations of PM2.5, CO, NO2, and O3 were documented from two metrological websites. Data were obtained from the date of the appearance of the first case of (SARS-CoV-2) in the five regions of the USA from 13 March to 31 December 2020. Regionally (Los Angeles, New Mexico, New York, Ohio, and Florida), the number of cases and deaths increased significantly along with increasing levels of PM2.5, CO, NO2 and O3 (p < 0.05), respectively. The Poisson regression results further depicted that, for each 1 unit increase in PM2.5, CO, NO2 and O3 levels, the number of SARS-CoV-2 infections significantly increased by 0.1%, 14.8%, 1.1%, and 0.1%, respectively; for each 1 unit increase in CO, NO2, and O3 levels, the number of deaths significantly increased by 4.2%, 3.4%, and 1.5%, respectively. These empirical estimates demonstrate an association between the environmental pollutants PM2.5, CO, NO2, and O3 and SARS-CoV-2 infections, showing that they contribute to the incidence of daily cases and daily deaths in the five different regions of the USA. These findings can inform health policy decisions about combatting the COVID-19 pandemic outbreak in these USA regions and internationally by supporting a reduction in environmental pollution.

Sandstorm and its effect on particulate matter PM 2.5, carbon monoxide, nitrogen dioxide, ozone pollutants and SARS-CoV-2 cases and deaths.

Sandstorms are a natural metrological phenomenon, frequently occurring in many arid and semi-arid regions of the world. The sandstorm dust contains environmental pollutants, microorganisms including bacteria, fungi, and viruses. These events are the primary sources of air pollution and its long-distance transport. Thus, sandstorms are becoming a greater concern during the COVID-19 pandemic. Therefore, this novel study aimed to investigate the effect of a sandstorm on "environmental pollutants particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and day-to-day new cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection" in Riyadh, Saudi Arabia. On March 12, 2021, a sandstorm occurred in the Riyadh region, the capital city of Saudi Arabia. The data on PM 2.5, CO, NO2, and O3 were recorded three weeks before and three weeks after the onset of the sandstorm, from February 20, 2021, to March 12, 2021, and from March 13 to April 2, 2021. The daily PM2.5, CO, NO2, and O3 levels were documented from the metrological websites, and Air Quality Index-AQI, COVID-19 daily cases, and deaths were obtained from Saudi Arabia's official coronavirus website. After sandstorm, the air pollutants, CO level increased by 84.25%; PM2.5: 76.71%; O3: 40.41%; NO2: 12.03%; and SARS-CoV-2 cases increased by 33.87%. However, the number of deaths decreased by 22.39%. The sandstorm event significantly increased the air pollutants, PM2.5, CO, and O3, which were temporally associated with increased SARS-COV-2 cases. However, no significant difference was noticed in NO2 and the number of deaths after the sandstorm. The findings have an important message to health authorities to timely provide information to the public about the sandstorm and its associated health problems, including SARS-CoV-2 cases and deaths.

Appropriateness of lung ultrasound for the diagnosis of COVID-19 pneumonia.

BACKGROUND: Chest radiography (CXR) and computerized tomography (CT) are the standard methods for lung imaging in diagnosing COVID-19 pneumonia in the intensive care unit (ICU), despite their limitations. This study aimed to assess the performance of bedside lung ultrasound examination by a critical care physician for the diagnosis of COVID-19 pneumonia during acute admission to the ICU. METHOD: This was an observational, prospective, single-center study conducted in the intensive care unit of Adan General Hospital from April 10, 2020, to May 26, 2020. The study included adults with suspicion of COVID-19 Infection who were transferred to the ICU. Patients were admitted to the ICU directly from the ED after reverse transcriptase-polymerase chain reaction (RT-PCR) swabs were sent to the central virology laboratory in Kuwait, and the results were released 16 to 24 hours after the time of admission. A certified intensivist in critical care ultrasound performed the lung ultrasound within 12 hours of the patient's admission to the ICU.The treating physician confirmed the diagnosis of COVID-19 pneumonia based on a set of clinical features, inflammatory markers, biochemical profile studies, RT-PCR test results, and CXR. RESULTS: Of 77 patients with suspected COVID-19 pneumonia, 65 (84.4%) were confirmed. The median age of the patients was 48 (31-68) years, and 51 (71%) were men.In the group of patients with confirmed COVID-19 pneumonia, LUS revealed four signs suggestive of COVID-19 pneumonia in 63 patients (96.9%) (sensitivity 96.9%, CI 85%-99.5%). Two patients presented with unilateral lobar pneumonia without other ultrasonic signs of COVID-19 pneumonia but with positive RT-PCR results. Among patients in the group without COVID-19 pneumonia who had negative RT-PCR results, 11 (91.7%) were LUS negative for COVID-19 pneumonia (specificity 91.7%, 95% CI 58.72%-99.77%). CONCLUSIONS: During the COVID-19 outbreak, LUS allows the identification of early signs of interstitial pneumonia. LUS patterns that show a combination of the four major signs offer high sensitivity and specificity compared to nasopharyngeal RT-PCR.

COVID-19 Contact Tracing Using Blockchain.

Contact tracing has widely been adopted to control the spread of Coronavirus-2019 (COVID-19). It enables to identify, assess, and manage people who have been exposed to COVID-19, thereby preventing from its further transmission. Today's most of the contact tracing approaches, tools, and solutions fall short in providing decentralized, transparent, traceable, immutable, auditable, secure, and trustworthy features. In this paper, we propose a decentralized blockchain-based COVID-19 contact tracing solution. Contact tracing can greatly suffice the need for a speedy response to a pandemic. We leverage the immutable and tamper-proof features of blockchain to enforce trust, accountability, and transparency. Trusted and registered oracles are used to bridge the gap between on-chain and off-chain data. With no third parties involved or centralized servers, the users' medical information is not prone to invasion, hacking, or abuse. Each user is registered using their digital medical passports. To respect the privacy of the users, their locations are updated with a time delay of 20 minutes. Using Ethereum smart contracts, transactions are executed on-chain with emitted events and immutable logs. We present details of the implemented algorithms and their testing analysis. We evaluate the proposed approach using security, cost, and privacy parameters to show its effectiveness. The smart contracts code is publicly made available on GitHub.

Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies.

The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today's systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub.

Lung Ultrasound and Sonographic Subpleural Consolidation in COVID-19 Pneumonia Correlate with Disease Severity.

INTRODUCTION: One of the ultrasonic features of COVID-19 pneumonia is the presence of subpleural consolidation (SPC), and the number of SPCs varies among patients with COVID-19 pneumonia. AIM: To examine the relationship between disease severity and the number of SPCs on admission. Methodology. This observational, prospective, single-center study included patients with suspected COVID-19 infection who had been transferred to the ICU. A specialized intensivist in critical care ultrasound performed lung ultrasound (LUS) and echocardiography within 12 hours of a patient's admission to the ICU. The aeration score was calculated, and the total number of SPCs was quantified in 12 zones of the LUS. RESULTS: Of 109 patients with suspected COVID-19 pneumonia, 77 (71%) were confirmed. The median patient age was 53 (82-36) years, and 81 of the patients (73.7%) were men. The aeration score and the counts of subpleural consolidation in each zone were significantly higher in patients with COVID-19 pneumonia (P=0.018 and P < 0.0001, respectively). There was an inverse relationship between PO2/FiO2, the aeration score, and the number of subpleural consolidations. The higher the number of SPCs, the worse the PO2/FiO2 will be. CONCLUSIONS: Sonographic SPC counts correlate well with the severity of COVID-19 pneumonia and PO2/FiO2. The number of SPCs should be considered when using LUS to assess disease severity.

Point-of-Care Ultrasound for COVID-19 Pneumonia Patients in the ICU.

BACKGROUND: There is scarce literature on point-of-care ultrasound (POCUS) assessment characteristics in coronavirus disease 2019 (COVID-19) pneumonia with hypoxic respiratory failure. METHODS: This study was an observational, prospective, single-center study, including adults suspected to have COVID-19 who were transferred to the intensive care unit (ICU). An intensivist in critical care ultrasound performed lung ultrasound (LUS) and echocardiology within 12 hours of patients' admission to the ICU. We calculated the trans mitral E/A ratio, E/e', left ventricular ejection fraction (EF), inferior vena cava (IVC) diameter, right ventricle (RV) size and systolic function. RESULTS: In the group of patients with confirmed COVID-19 pneumonia, echocardiographic findings revealed normal E/e', deceleration time (DT), and transmittal E/A ratio compared to those in the non-COVID-19 patients (p = 0.001, 0.0001, and 0.0001, respectively). IVC diameter was < 2 cm with > 50% collapsibility in 62 (81%) patients with COVID-19 pneumonia; a diameter of > 2 cm and < 50% collapsibility was detected among those with non-COVID-19 pneumonia (p-value of 0.001). In patients with COVID-19 pneumonia, there were 3 cases of myocarditis (3.9%) with poor EF, severe RV systolic dysfunction was seen in 9 cases (11.6%), and 3 cases exhibited RV thrombus. Lung US revealed 4 signs suggestive of COVID-19 pneumonia in 77 patients (98.6%) (sensitivity 96.9%; confidence interval, 85%-99.5%) when compared with reverse transcriptase-polymerase chain reaction results. CONCLUSIONS: POCUS plays an important role in the bedside diagnosis, hemodynamic assessment and management of patients with acute hypoxic respiratory and circulatory failure with COVID-19 pneumonia.

The role of blockchain technology in telehealth and telemedicine.

OBJECTIVE: Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID-9. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. METHODS: The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. RESULTS: Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis. CONCLUSIONS: Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.

Simulation and Evaluation of Precaution Strategies for COVID-19 Pandemic: A Case Study (preprint)

The world now is living in Corona Virus Disease (COVID-19) pandemic era, which forced almost all businesses and companies to lockdown, which as a result led to economic fluctuation and social issues. Some precautions strategies were introduced and applied by governments to allow businesses to reopen to the public. This paper aims to model an agent-based crowd simulation using unity game engine with C# programming language in order to explore the effectiveness of the current precautions to limit the spread of COVID-19. Two important models are adopted in this work, which are the SIR disease spreading model and the homogeneous mixing behavior model. Utilizing these models, several parameters are chosen to control the viral spread among the agents in five different realistic scenarios. After several simulations, it was determined that the number of agents in the scene and wearing masks have the highest impact on limiting the viral spread.

Blockchain-Based Solution for COVID-19 Digital Medical Passports and Immunity Certificates.

COVID-19 has emerged as a highly contagious disease which has caused a devastating impact across the world with a very large number of infections and deaths. Timely and accurate testing is paramount to an effective response to this pandemic as it helps identify infections and therefore mitigate (isolate/cure) them. In this paper, we investigate this challenge and contribute by presenting a blockchain-based solution that incorporates self-sovereign identity, re-encryption proxies, and decentralized storage, such as the interplanetary file systems (IPFS). Our solution implements digital medical passports (DMP) and immunity certificates for COVID-19 test-takers. We present smart contracts based on the Ethereum blockchain written and tested successfully to maintain a digital medical identity for test-takers that help in a prompt trusted response directly by the relevant medical authorities. We reduce the response time of the medical facilities, alleviate the spread of false information by using immutable trusted blockchain, and curb the spread of the disease through DMP. We present a detailed description of the system design, development, and evaluation (cost and security analysis) for the proposed solution. Since our code leverages the use of the on-chain events, the cost of our design is almost negligible. We have made our smart contract codes publicly available on Github.

Effect of environmental pollutants PM-2.5, carbon monoxide, and ozone on the incidence and mortality of SARS-COV-2 infection in ten wildfire affected counties in California.

Various regions of California have experienced a large number of wildfires this year, at the same time the state has been experiencing a large number of cases of and deaths from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The present study aimed to investigate the relationship of wildfire allied pollutants, including particulate matter (PM-2.5 µm), carbon monoxide (CO), and Ozone (O3) with the dynamics of new daily cases and deaths due to SARS-COV 2 infection in 10 counties, which were affected by wildfire in California. The data on COVID-19 pertaining to daily new cases and deaths was recorded from Worldometer Web. The daily PM-2.5 µm, CO, and O3 concentrations were recorded from three metrological websites: BAAQMD- Air Quality Data; California Air Quality Index-AQI; and Environmental Protection Agency- EPA. The data recorded from the date of the appearance of first case of (SARS-CoV-2) in California region to the onset of wildfire, and from the onset of wildfire to September 22, 2020. After the wildfire, the PM2.5 concentration increased by 220.71%; O3 by 19.56%; and the CO concentration increased by 151.05%. After the wildfire, the numbers of cases and deaths due to COVID-19 both increased respectively by 56.9% and 148.2%. The California wildfire caused an increase in ambient concentrations of toxic pollutants which were temporally associated with an increase in the incidence and mortality of COVID-19.

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges (preprint)

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make a prediction about the event. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make a prediction about the event. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.