Association of d-dimer levels with in-hospital outcomes among COVID-19 positive patients: a developing country multicenter retrospective cohort.

D-dimer levels, which originate from the lysis of cross-linked fibrin, are serially measured during coronavirus disease 2019 illness to rule out hypercoagulability as well as a septic marker. Methods: This multicenter retrospective study was carried out in two tertiary care hospitals in Karachi, Pakistan. The study included adult patients admitted with a laboratory-confirmed coronavirus disease 2019 infection, with at least one measured d-dimer within 24 h following admission. Discharged patients were compared with the mortality group for survival analysis. Results: The study population of 813 patients had 68.5% males, with a median age of 57.0 years and 14.0 days of illness. The largest d-dimer elevation was between 0.51-2.00 mcg/ml (tertile 2) observed in 332 patients (40.8%), followed by 236 patients (29.2%) having values greater than 5.00 mcg/ml (tertile 4). Within 45 days of hospital stay, 230 patients (28.3%) died, with the majority in the ICU (53.9%). On multivariable logistic regression between d-dimer and mortality, the unadjusted (Model 1) had a higher d-dimer category (tertile 3 and tertile 4) associated with a higher risk of death (OR: 2.15; 95% CI: 1.02-4.54, P=0.044) and (OR: 4.74; 95% CI: 2.38-9.46, P<0.001). Adjustment for age, sex, and BMI (Model 2) yields only tertile 4 being significant (OR: 4.27; 95% CI: 2.06-8.86, P<0.001). Conclusion: Higher d-dimer levels were independently associated with a high risk of mortality. The added value of d-dimer in risk stratifying patients for mortality was not affected by invasive ventilation, ICU stays, length of hospital stays, or comorbidities.

On the Adoption of Modern Technologies to Fight the COVID-19 Pandemic: A Technical Synthesis of Latest Developments

In the ongoing COVID-19 pandemic, digital technologies have played a vital role to minimize the spread of COVID-19, and to control its pitfalls for the general public. Without such technologies, bringing the pandemic under control would have been tricky and slow. Consequently, exploration of pandemic status, and devising appropriate mitigation strategies would also be difficult. In this paper, we present a comprehensive analysis of community-beneficial digital technologies that were employed to fight the COVID-19 pandemic. Specifically, we demonstrate the practical applications of ten major digital technologies that have effectively served mankind in different ways during the pandemic crisis. We have chosen these technologies based on their technical significance and large-scale adoption in the COVID-19 arena. The selected technologies are the Internet of Things (IoT), artificial intelligence(AI), natural language processing(NLP), computer vision (CV), blockchain (BC), federated learning (FL), robotics, tiny machine learning (TinyML), edge computing (EC), and synthetic data (SD). For each technology, we demonstrate the working mechanism, technical applications in the context of COVID-19, and major challenges from the perspective of COVID-19. Our analysis can pave the way to understanding the roles of these digital COVID-19-fighting technologies that can be used to fight future infectious diseases to prevent global crises. Moreover, we discuss heterogeneous data that have significantly contributed to addressing multiple aspects of the ongoing pandemic when fed to the aforementioned technologies. To the best of the authors' knowledge, this is a pioneering work on community-beneficial and transformative technologies in the context of COVID-19 with broader coverage of studies and applications.

Technical Analysis of Contact Tracing Platform Developed by Google-Apple for Constraining the Spread of COVID-19

Amid the ongoing COVID-19 pandemic, technical solutions (e.g., smartphone apps, web-based platforms, digital surveillance platforms, etc.) have played a vital role in constraining the spread of COVID-19. The major aspects in which technical solutions have helped the general public (or health officials) are contact tracing, spread prediction, trend forecasting, infection risk estimation, hotspot identification, alerting people to stay away from contaminated places, hospitalization length estimation, clinical severity analysis, and quarantine monitoring, to name a few. Apart from other services, contact tracing has been extensively performed with the help of Bluetooth and GPS-powered smartphone applications when vaccines were unavailable. In this article, we technically analyze the contact tracing platform developed by Google-Apple for constraining the spread of COVID-19. We suggest unexplored technical functionalities that can further strengthen the platform from privacy preservation, service scenarios, and robustness point of view. Lastly, some AI-based and privacy-assured services that can be integrated with the platform to control the pandemic adequately are suggested. The technical analysis demonstrates that while the Google-Apple platform is well-engineered, it is not free of vulnerabilities, weaknesses, and misconfigurations that may lead to its poor adoption in real-life scenarios. This work can serve as a guideline for further enhancing the practicality of contact tracing platform to effectively handle future infectious diseases.

Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19

Federated learning (FL) is one of the leading paradigms of modern times with higher privacy guarantees than any other digital solution. Since its inception in 2016, FL has been rigorously investigated from multiple perspectives. Some of these perspectives are extensions of FL's applications in different sectors, communication overheads, statistical heterogeneity problems, client dropout issues, the legitimacy of FL system results, privacy preservation, etc. Recently, FL is being increasingly used in the medical domain for multiple purposes, and many successful applications exist that are serving mankind in various ways. In this work, we describe the novel applications and challenges of the FL paradigm with special emphasis on the COVID-19 pandemic. We describe the synergies of FL with other emerging technologies to accomplish multiple services to fight the COVID-19 pandemic. We analyze the recent open-source development of FL which can help in designing scalable and reliable FL models. Lastly, we suggest valuable recommendations to enhance the technical persuasiveness of the FL paradigm. To the best of the authors' knowledge, this is the first work that highlights the efficacy of FL in the era of COVID-19. The analysis enclosed in this article can pave the way for understanding the technical efficacy of FL in medical field, specifically COVID-19.

A Privacy-Assured Data Lifecycle for Epidemic-Handling Systems

Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs.

Impact of consumption values on consumers’ purchase of organic food and green environmental concerns

Purpose>This study aims to examine the impact of consumption values on consumers’ purchase of organic food and green environmental concerns. Additionally, the relationships between green environmental concerns and consumers’ purchase of organic food are investigated.Design/methodology/approach>A self-administered questionnaire was distributed to 500 consumers with experience in purchasing organic food in Pakistan. The covariance-based structural equation modeling (CB-SEM) technique was used for the data analysis using the Analysis of Moments Structure software version 23. The CB-SEM technique allows for the simultaneous estimation of all relationships.Findings>The CB-SEM technique reveals that of the 11 hypotheses tested, social value heavily influences consumers’ green environmental concerns. Moreover, consumers’ purchase of organic food is greatly impacted by conditional value. Consumers purchase organic food for their daily needs because they feel responsible for preserving and protecting the environment against global warming and its associated threats. This green purchasing behavior actually leads to better social approval, through its ability to impress others.Practical implications>Organizations and business owners should address green environmental concerns by seriously applying organic methods in the process of production, processing, packaging and selling of organic food products. Such organic practices would enable organizations and business owners to produce organic food products that are free from chemicals.Originality/value>The inclusion of consumption values strengthens the explanatory power of the proposed model in the context of Pakistani consumers’ purchase of organic food and green environmental concerns simultaneously. This study therefore adds new and substantial insights into the marketing theory.

Assessing the Impact of COVID-19 on the Mental Health of Healthcare Workers in Three Metropolitan Cities of Pakistan.

PURPOSE: The COVID-19 (coronavirus disease-2019) has been associated with psychological distress during its rapid rise period in Pakistan. The present study aimed to assess the mental health of healthcare workers (HCWs) in the three metropolitan cities of Pakistan. METHODS: A cross-sectional, web-based study was conducted in 276 HCWs from April 10, 2020, to June 5, 2020. Depression, anxiety, and stress scale (DASS-21) were used for the mental health assessment of the HCWs. Multivariable logistic regression analysis (MLRA) was performed to measure the association between the demographics and the occurrence of depression, anxiety, and stress (DAS). RESULTS: The frequency of DAS in the HCWs was 10.1%, 25.4%, and 7.3%, respectively. The MLRA showed that the depression in HCWs was significantly associated with the profession (P<0.001). The anxiety in HCWs was significantly associated with their age (P=0.005), profession (P<0.05), and residence (P<0.05). The stress in HCWs was significantly associated with their age (P<0.05). LIMITATION: This study was conducted in the early phase of the COVID-19 pandemic, when the number of COVID-19 cases was on the rise in Pakistan and it only represents a definite period (April to June 2020). CONCLUSION: The symptoms of DAS are present in the HCWs of Pakistan and to manage the psychological health of HCWs, there is a need for the initiation of psychological well-being programs.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

Effective Handling of COVID-19 Pandemic: Experiences and Lessons from the Perspective of South Korea

The ongoing pandemic (i.e., novel corona virus disease 2019 (COVID-19)) is a major crisis that humanity is facing across the globe. In response to COVID-19, every country has designed and enforced various mechanisms to control its spread, and save their citizens from this deadly disease. Besides the general containment strategies, the use of technology and person-specific data collection and processing also vary from country to country. In this brief case report, we describe the measures that South Korea (SK) has adopted in order to curb the spread of COVID-19, and the success of SK in keeping the cases at a manageable level since the start of this pandemic until present via extensive use of technology. Specifically, we describe the data collected to control this pandemic, technical framework in place to ensure co-ordination with different authorities, containment strategies, and major breakthrough SK achieved when sporadic clusters emerged throughout the nation. With this brief overview, we aim to update the research community about the unprecedented efforts SK made, utilizing previous pandemic experiences, and the successful results they have obtained so far amid this pandemic, leveraging technology.

A National Survey to Assess the COVID-19 Vaccine-Related Conspiracy Beliefs, Acceptability, Preference, and Willingness to Pay among the General Population of Pakistan.

The current study aims to assess the beliefs of the general public in Pakistan towards conspiracy theories, acceptance, willingness to pay, and preference for the COVID-19 vaccine. A cross-sectional study was conducted through an online self-administered questionnaire during January 2021. The Chi-square test or Fisher exact test was utilized for statistical data analysis. A total of 2158 respondents completed the questionnaire, among them 1192 (55.2%) were male with 23.87 (SD: ±6.23) years as mean age. The conspiracy beliefs circulating regarding the COVID-19 vaccine were believed by 9.3% to 28.4% of the study participants. Among them, 1040 (48.2%) agreed to vaccinate on its availability while 934 (43.3%) reported the Chinese vaccine as their preference. The conspiracy beliefs of the participants were significantly associated with acceptance of the COVID-19 vaccine. The existence of conspiracy beliefs and low vaccine acceptance among the general population is a serious threat to successful COVID-19 vaccination.

Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

Assessing Barriers Faced by Surgeons While Providing Surgical Care During the COVID-19 Pandemic in Pakistan: An Online Cross-Sectional Study.

BACKGROUND: The COVID-19 pandemic is not only affecting public health, but it is also impairing the specialized surgical care services in the hospitals. The present study aimed to assess the barriers faced by the surgeons while performing surgical procedures during the COVID-19 pandemic. METHODS: A cross-sectional, web-based survey was conducted from September 10 to October 14, 2020. The study population consisted of surgeons practicing in Kpk, Pakistan. Descriptive statistics and binary logistic regression analysis were used to analyze the data. RESULTS: A total of 292, out of 543, surgeons participated in the study (response rate: 59.6%). The younger surgeons (25-30 years) considered the lack of policies and practices regarding exposure to COVID-19 patients as a significant barrier to their practice. The surgeons practicing in private hospitals considered themselves at a higher risk while providing surgical care to the COVID-19 patients. The non-cooperation of the patients was the main barrier in delivering surgical care services. CONCLUSION AND RECOMMENDATION: The current study highlighted the barriers to the surgeons while providing surgical care to patients in the current pandemic. The most pronounced barriers to the surgeons were the lack of policies regarding exposure to COVID-19 and practice and non-cooperation of the patient. To address these barriers, it is recommended that health regulatory agencies of Pakistan should implement strict infection control practices to ensure the safety of surgeons and allied healthcare staff during the COVID-19 pandemic.

Does air pollution upsurge in megacities after Covid-19 lockdown? A spatial approach.

The current coronavirus (COVID-19) pandemic has a high spreading and fatality rate. To control the rapid spreading of the COVID-19 virus, the government of India imposed lockdown policies, which creates a unique opportunity to analyze the impact of lockdown on air quality in the two most populous cities of India, i.e., Delhi and Mumbai. To do this, the study employed a spatial approach to examine the concentration of seven criteria pollutants, i.e., PM2.5, PM10, NH3, CO, NO2, O3, and SO2, before, during, and after a lockdown in Delhi and Mumbai. Overall, around 42%, 50%, 21%, 37%, 53%, and 41% declines in PM2.5, PM10, NH3, CO, NO2, and SO2 were observed during the lockdown period as compared to previous years. On the other hand, a 2% increase in O3 concentration was observed. However, the study analyzed the National Air Quality Index (NAQI) for Delhi and Mumbai and found that lockdown does not improve the air quality in the long term period. Our key findings provide essential information to the cities' administration to develop rules and regulations to enhance air quality.

Knowledge, Attitudes, and Clinical Practices of Dental Professionals during COVID-19 Pandemic in Pakistan.

OBJECTIVE: The aim of this study is to assess knowledge, attitudes, and clinical practices of dental professionals regarding the prevention and control of coronavirus disease 2019 (COVID-19) in Pakistan. MATERIALS AND METHODS: General dentists and dental specialists working in public and private dental practices, hospitals, and academic institutions participated in this cross-sectional study. A pilot-tested questionnaire was sent to dental professionals through an online link in Pakistan and data collection was completed in April-May 2020. The knowledge score was calculated from 22 variables about the COVID-19. RESULTS: The study included data of 343 dental professionals with 47.2% of males and 52.8% of females. The mean knowledge score was 16.78 ± 2.25, and it significantly differed between general dentists (16.55 ± 2.36) and dental specialists (17.15 ± 2.04) (p = 0.020), and those with up to 10 years of experience (16.58 ± 2.28) and those with more than 10 years of experience (17.05 ± 2.2) (p = 0.026). Only 15.5% of the participants were comfortable in treating patients during the COVID-19 pandemic. A workshop/seminar on the COVID-19 was attended by 23% of the participants. In multivariate analysis, being comfortable in treating patients (odds ratio = 3.31, 95% confidence interval = 1.63, 6.73) was associated with the attendance of workshop/seminar on COVID-19. CONCLUSIONS: Dental professionals had adequate knowledge about COVID-19, but a few of them were comfortable in treating patients during the pandemic. A minority of dental professionals attended a workshop/seminar on the COVID-19. Continuous education activities should be provided to dental professionals to enhance their role in the prevention of COVID-19 spread and promotion of oral health.

A national study to assess pharmacists' preparedness against COVID-19 during its rapid rise period in Pakistan.

To evaluate the pharmacist's preparedness against the COVID-19 during its rapid rise period in Pakistan, an online cross-sectional study was carried out from March 30 to May 22, 2020 among the pharmacists using a pre-validated self-administered questionnaire. A total of 1149 participants completed the survey, amongst which 430(37.9%) were working as retail pharmacists, 216 (18.8%) as community pharmacists, and 213(18.5%) as hospital pharmacists. The mean COVID-19 knowledge score of the participants was 6.77±0.5, which indicated that 84% of them had good knowledge about COVID-19. The multiple linear regression model revealed that attitude was significantly associated with gender (p = 0.001), marital status (p<0.0001) and resident (p = 0.013). The mean practice score was 2.85±0.4, showing that 94% of the participants were following adequate preventive practices against this infection. The results from this study suggest that Pharmacists demonstrated good knowledge, positive attitudes, and acceptable practices regarding COVID-19.

Knowledge, Attitude, and Practices Toward COVID-19 in Primary Healthcare Providers: A Cross-Sectional Study from Three Tertiary Care Hospitals of Peshawar, Pakistan.

An online cross-sectional study was carried out to evaluate the knowledge, attitude, and practice about coronavirus disease 2019 (COVID-19) among primary health care providers (PHPs) at three tertiary care hospital, Peshawar, Pakistan. Data was collected via email and online social media platforms. Statistical package for social science (SPSS) version 25.0 was used for data analysis. Among the total participants (n = 114), 74 (66.7%) were male and 37 (33.3%) were female. The mean scores for knowledge, attitude and practice were 12.7 ± 0.89, 8.9 ± 4.1 and 7.3 ± 1.2, respectively. Most of the participants knew the term COVID-19 and its mode of transmission (90%), signs and symptoms (84%) and risk factors (72%) associated with it. Most of the participants agreed that COVID-19 can be transmitted through coughing and sneezing (74.3%) and 84.6% were in favor that COVID-19 can be prevented by adopting preventive measures. Around 68.8% of the participants disagreed with the use of antibiotics in the prevention of COVID-19. Ninety percent of the respondents were avoiding close contact with the people having cough and flu-like symptoms. Most PHPs had good knowledge, positive attitude and reasonable practices regarding COVID-19. Moreover, focused training programs for PHPs at the Government level can further improve their understanding of risks and preventive strategies related to COVID-19, which will help them to provide appropriate care to their patients as well as to protect themselves from this infection.