Haytham A. Sheerah, Ahmed Arafa, Mansour A. Alfaya, Ashraf B AlDerbas · 10 authors
Problem: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. Approach: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. Local setting: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. Relevant changes: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. Lessons learnt: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making.
Abstract Bitcoin and Ethereum are the two largest cryptocurrencies in the world by market capitalization and trading volume and the most popular despite high price fluctuations. This paper analyzes the relationship between Bitcoin and Ethereum metrics and the internet search interest on cryptocurrencies. As the literature shows, Google searches signal investor attention and Google Trends has proven useful for nowcasting economic and financial indicators. We aim to find the impact of Google Trends on Bitcoin and Ethereum prices, trading volumes and market capitalization since 2015 and discuss the potential correlations and patterns that may exist between these metrics and Google search interest. Through correlation and time-series analysis, we provide insights into the dynamics of this relationship and its implications for understanding cryptocurrency market behavior. The interest in cryptocurrencies tracked by Google Trends is a good indicator of measuring the social interest in the cryptocurrency market that drives a price movement. On the other hand, the price fluctuations of Bitcoin and Ethereum generate media and social attention and increase the interest in these cryptocurrencies. We also observe a positive effect of Google Trends values on trading volumes. The findings could help investors to understand the cryptocurrencies dynamics and build their trading strategies and could be of special interest to policymakers.
IOTA is a distributed ledger technology that uses a Directed Acyclic Graph (DAG) structure called the Tangle. It is known for its efficiency and is widely used in the Internet of Things (IoT) environment. Tangle can be configured by utilizing the tip selection process. Due to performance issues with light nodes, full nodes are being asked to perform the tip selections of light nodes. However, in this paper, we demonstrate that tip selection can be exploited to compromise users' privacy. An adversary full node can associate a transaction with the identity of a light node by comparing the light node's request with its ledger. We show that these types of attacks are not only viable in the current IOTA environment but also in IOTA 2.0 and the privacy improvement being studied. We also provide solutions to mitigate these attacks and propose ways to enhance anonymity in the IOTA network while maintaining efficiency and scalability.
The integration of blockchain technology in biomedical diagnostics offers a promising solution to the challenges of data security and privacy in infectious disease surveillance. As the digitalization of healthcare systems accelerates, the need to protect sensitive health information becomes increasingly critical. Blockchain, with its decentralized and immutable nature, provides a robust framework for ensuring the integrity and confidentiality of biomedical data. This abstract explores how blockchain technology can be leveraged to enhance data security and privacy in the context of infectious disease surveillance, where rapid and accurate data sharing is essential for effective public health responses. Infectious disease surveillance relies on the collection, analysis, and dissemination of large volumes of data, often shared across multiple institutions and geographical regions. Traditional systems for managing this data are vulnerable to breaches, unauthorized access, and data tampering, which can compromise public health efforts and patient privacy. Blockchain technology addresses these vulnerabilities by enabling secure, transparent, and tamper-proof data exchanges. Each transaction or data entry is recorded in a distributed ledger, accessible only to authorized participants, thus ensuring that the data remains secure and unaltered. Moreover, blockchainâs inherent transparency allows for real-time monitoring and auditing of data flows, which is crucial in the timely detection and response to infectious disease outbreaks. The use of smart contracts within blockchain networks further enhances the automation and efficiency of data management, ensuring that data is only accessed and shared according to predefined rules and conditions. This not only safeguards patient privacy but also builds trust among stakeholders, including patients, healthcare providers, and public health authorities. In conclusion, the integration of blockchain technology in biomedical diagnostics presents a transformative approach to addressing the critical issues of data security and privacy in infectious disease surveillance. By leveraging blockchain's unique features, healthcare systems can ensure that sensitive diagnostic data is protected, thus supporting more effective and secure public health interventions in the fight against infectious diseases. Keywords: One Health Approach, Zoonotic Disease, Early Detection, Development, Portable Diagnostic Device.
Nektarios Aslanidis, Aurelio F. Bariviera, Ăscar G. LĂłpez
This paper revisits the linkage between cryptocurrencies and public disclosed preferences, proxied by online searches. We show that cryptocurrencies are not related to a general uncertainty index as measured by the Google Trends data by Castelnuovo and Tran (2017). Instead, cryptocurrencies are linked to a Google Trends attention measure specific for this market. In particular, we find a bidirectional flow of information between Google Trends attention and cryptocurrency returns up to six days. Moreover, information flows from cryptocurrency volatility to Google Trends attention seem to be larger than those in the other direction. Finally, we report a significant tail dependence between cryptocurrency returns and Google Trends. These relations hold for the five cryptocurrencies analyzed and different compositions of the proposed Google Trends Cryptocurrency index.
Mohamed Torky, Essam Goda, Våclav SnÄƥel, 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.
Nektarios Aslanidis, Aurelio F. Bariviera, Ăscar G. LĂłpez
This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index.
Pedro Elkind Velmovitsky, Frederico M. Bublitz, Laura Fadrique, Plinio Pelegrini Morita
BACKGROUND Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges. OBJECTIVE In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies. METHODS We conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium. RESULTS Health care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a productâs provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process. CONCLUSIONS Blockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint. CLINICALTRIAL
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Alexandros Bampoulidis, A. Bruni, Lukas Helminger, Daniel Kales · 6 authors
Recent work has shown that cell phone mobility data has the unique potential\nto create accurate models for human mobility and consequently the spread of\ninfected diseases. While prior studies have exclusively relied on a mobile\nnetwork operator's subscribers' aggregated data in modelling disease dynamics,\nit may be preferable to contemplate aggregated mobility data of infected\nindividuals only. Clearly, naively linking mobile phone data with health\nrecords would violate privacy by either allowing to track mobility patterns of\ninfected individuals, leak information on who is infected, or both. This work\naims to develop a solution that reports the aggregated mobile phone location\ndata of infected individuals while still maintaining compliance with privacy\nexpectations. To achieve privacy, we use homomorphic encryption, validation\ntechniques derived from zero-knowledge proofs, and differential privacy. Our\nprotocol's open-source implementation can process eight million subscribers in\n70 minutes.\n
The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.
Blockchain technology has an enormous scope to revamp the healthcare system in many ways as it improves the quality of healthcare by data sharing among all the participants, selective privacy and ensuring data safety. This paper explores the basics of blockchain, its applications, quality of experience and advantages in disease surveillance over the other widely used real-time and machine learning techniques. The other real-time surveillance systems lack scalability, security, interoperability, thus making blockchain as a choice for surveillance. Blockchain offers the capability of enhancing global health security and also can ensure the anonymity of patient data thereby aiding in healthcare research. The recent epidemics of re-emerging infections such as Ebola and Zika have raised many concerns regarding health security which resulted in strengthening the surveillance systems. We also discuss how blockchains can help in identifying the threats early and reporting them to health authorities for taking early preventive measures. Since the Global Health Security Agenda addresses global public health threats (both infectious and NCDs); strengthen the workforce and the systems; detect and respond rapidly and effectively to the disease threats; and elevate global health security as a priority. The blockchain has enormous potential to disrupt many current practices in traditional disease surveillance and health care research.
Die Unsicherheit ĂŒber den intrinsischen Wert von KryptowĂ€hrungen und der nachgewiesene Einfluss der Aufmerksamkeit an dem Marktwert verschiedener Vermögenswerte haben uns veranlasst, den Einfluss der Aufmerksamkeit auf den Marktwert von KryptowĂ€hrungen zu untersuchen. Als Aufmerksamkeitsindikator haben wir das Volumen von Google-Suchen zu bestimmten der Suchwörter Suchbegriffe genutzt das Suchvolumen von Google-Suchmenge, die auf SchlĂŒsselwörtern basieren, die sich auf unseren Satz von KryptowĂ€hrungen einer sehr genauen GranularitĂ€t beziehen. Unter Verwendung von ARMA und VECM haben wir getestet, ob die Google-Suchmenge die Vorhersage fĂŒr KryptowĂ€hrungspreisentwicklung im Zeitrahmen von 15 Minuten bis zu einem Tag verbessert. AnschlieĂend haben wir den Handel mit dieser Out-of-Sample Prognose simuliert und kamen zu dem Schluss, dass im Fall von hĂ€ufigem Handel ohne GebĂŒhren, einfache, univariate, autoregressive Modelle besser Ergebnisse produzieren. Unter VernachlĂ€ssigung von GebĂŒhren jedoch, verbessert sich durch die Einbeziehung der Variablen fĂŒr das Google-Suchvolumen das Handelsergebnisse, insbesondere bei stĂŒndlichen und tĂ€glichen Frequenzen. Unter Verwendung solcher Frequenzen ĂŒbertraf das Modell univariate Modelle sowie das Wachstum der zugrunde liegenden Vermögenswerte.
Blockchain technology is a decentralized system of recording data and performing transactions which is increasingly being used across many industries, including healthcare. It has several unique features like the validation of transaction processes, prevention of systems failure from any single point of transaction, and approval of data sharing with optimal security, to name a few. At the hospital level, blockchain technologies are used in the electronic medical records systems, insurance claims, billing management, and so on. Moreover, this technology is helpful to manage logistic and human resources to achieve the quality of care in learning health systems. In many countries, blockchain is being used to promote patient-centered care by sharing patient data for remote monitoring and management. Furthermore, blockchain technology has the potential to strengthen disease surveillance systems in cases of disease outbreaks resulting in local and global health emergencies. In such conditions, blockchain can be used to identify health security concerns, analyze preventive measures, and facilitate decision-making processes to act rapidly and effectively. Despite its limitations, research, and practice based on blockchain technology have shown promises to strengthen health systems around the world with a potential to reduce the global burden of diseases, mortality, morbidity, and economic costs.
Ayushi Sharma, Shashwat Tiwari, Nitin Arora, S. C. Sharma
Blockchain is an emerging technology that can radically improve transactions security at banking, supply chain, and other transaction networks. It's estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering. This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack, and cryptocurrency including Facebook's Libra will be elucidated. Also, the book will address the relationship between Blockchain technology, Internet of Things (IoT), Artificial Intelligence (AI), Cybersecurity, Digital Transformation and Quantum Computing. Readers will understand the inner workings and applications of this disruptive technology and its potential impact on all aspects of the business world and society. A look at the future trends of Blockchain Technology will be presented in the book.
BACKGROUND A blockchain is a digitized, decentralized, distributed public ledger that acts as a shared and synchronized database that records cryptocurrency transactions. Despite the shift toward digital platforms enabled by electronic medical records, demonstrating a will to reform the health care sector, health systems face issues including security, interoperability, data fragmentation, timely access to patient data, and silos. The application of health care blockchains could enable data interoperability, enhancement of precision medicine, and reduction in prescription frauds through implementing novel methods in access and patient consent. OBJECTIVE To summarize the evidence on the strategies and frameworks utilized to implement blockchains for patient data in health care to ensure privacy and improve interoperability and scalability. It is anticipated this review will assist in the development of recommendations that will assist key stakeholders in health care blockchain implementation, and we predict that the evidence generated will challenge the health care status quo, moving away from more traditional approaches and facilitating decision making of patients, health care providers, and researchers. METHODS A systematic search of MEDLINE/PubMed, Embase, Scopus, ProQuest Technology Collection and Engineering Index will be conducted. Two experienced independent reviewers will conduct titles and abstract screening followed by full-text reading to determine study eligibility. Data will then be extracted onto data extraction forms before using the Cochrane Collaboration Risk of Bias Tool to appraise the quality of included randomized studies and the Risk of Bias in nonrandomized studies of Interventions to assess the quality of nonrandomized studies. Data will then be analyzed and synthesized. RESULTS Database searches will be initiated in September 2018. We expect to complete the review in January 2019. CONCLUSIONS This review will summarize the strategies and frameworks used to implement blockchains in health care to increase data privacy, interoperability, and scalability. This review will also help clarify if the strategies and frameworks required for the operationalization of blockchains in health care ensure the privacy of patient data while enabling efficiency, interoperability, and scalability.
Abstract Disease surveillance, especially for infectious diseases, is a complex and inefficient process. Here we propose an optimized, blockchain-based monitoring and reporting process which can achieve all the desired features of an ideal surveillance system while maintaining costs down and being transparent and robust. We describe the technical specifications of such a solution and discuss possibilities for its implementation. Finally, the impact of the adoption of distributed ledger technology for disease surveillance is discussed.
Jia Bainga Kangbai, P. Rohini Bai, Sulaiman Mandoh, Abu Bakarr Fofanah · 8 authors
Objective: In the absence of any approved therapeutics and vaccines to treat or prevent Ebola infection, managing Ebola outbreak largely depends on early case detection and surveillance, real-time communication of surveillance data, and Ebola case management. Here we assessed the possibility of uploading data obtained by Internet of Internet device that monitors cellphone companyâs Call Data Records (CDR), national demographic census, national transportation system and Ebola vaccine production databases on a Blockchain platform to conduct real-time Ebola contact tracing, transmission pattern surveillance and vaccine delivery. Results: Mobility data obtained by Internet of Things (IoT) from CDR from cellphone companies, national transportation system, and census demographic data can be integrated into a Blockchain platform to provide real-time Ebola surveillance and contact-tracing. While cellphone data provides a unique opportunity to quantify human mobility, Blockchain system magnifies such opportunity by making the data accessible to many actors in real-time. By mounting data from CDR, national population census, national transport system and Ebola vaccine production database on a Blockchain platform will provide additional lens in our understanding of the role played by human population dynamics in the spread as well and containment of Ebola during outbreaks
The anonymity of Bitcoin prevents analysis of its users. We collect Google Trends data to examine determinants of interest in Bitcoin. Based on anecdotal evidence regarding Bitcoin users, we construct proxies for four possible clientele: computer programming enthusiasts, speculative investors, Libertarians and criminals. Computer programming and illegal activity search terms are positively correlated with Bitcoin interest, while Libertarian and investment terms are not.
In June the Office of Management and Budget proposed new, congressionally mandated guidelines to ensure the quality of the data gathered and disseminated by federal agencies. The new law augments the Paperwork Reduction Act, which already requires federal agencies to adopt processes to ensure data quality. Many scientists, however, worry that the guidelinesâwhich require agencies to devise and implement procedures that make it possible for individuals to challenge the accuracy of agency informationâcould impede or prevent the flow of scientific information and undermine the peer review process. Comments submitted to OMB by 55 scientific societies and academic institutions, including the American Institute of Biological Sciences, supported the intent of the law. âAs biomedical researchers whose work depends on the excellence of our own data, we are acutely aware of and sensitive to the importance of accurate data,â wrote Robert Rich, M.D., president of the Federation of American Societies for Experimental Biology (FASEB). The problem, scientists say, is not with the intent of the law but with the vagueness of the guidelines OMB proposes for implementing it. In particular, scientists argue that challengers seeking a correction of an agency's data should have expertise in the subject matter and should disclose any financial interest at stake in the outcome of the challenge. There should be a clear burden of proof on the person or group who made the initial charge to demonstrate why the data being challenged are inaccurate or wrong, added Elaine Hoagland, national executive officer for the Council on Undergraduate Research. Further, the OMB rules should contain adequate safeguards against frivolous challenges of scientific information. Those safeguards should allow government agencies to insist on the same degree of rigor and objectivity required of the original science, says Stephen Heinig, senior staff associate at the Association of American Medical Colleges (AAMC). âScientists can fight out issues of data and methodology in the literature,â Heinig says. âThat's the standard. We don't see any reason to change it.â And, noted AAMC President Jordan Cohen, the procedures mandated by the guidelines will be burdensome to agencies, whose staffâalready struggling to meet core missionsâwill have to shoulder supervisory and reporting tasks that contribute little to the agencies' missions. Citing âerrorsâ in Environmental Protection Agency data, as well as the sheer volume of information that EPA and other government agencies post on the Internet, Jim Tozzi explains that âwe felt the need to ensure the quality of government data.â Tozzi, a former OMB official who is now a board advisor to the Center for Regulatory Effectiveness, an independent policy group that helped draft the new law, calls the debate over OMB's data quality proposals âa big hullabaloo over nothing.â Not so, say Washington's scientific and educational groups. âThere is plenty of potential here for mischief,â notes Richard Harpel, director of federal relations for the National Association of State Universities and Land Grant Colleges. In part, that is because the draft guidelines are very subjective, says Joanne Carney, director of the Center for Science, Technology and Congress at the American Academy for the Advancement of Science. Robert Wells, president of the American Society for Biochemistry and Molecular Biology, expressed the views of many scientific societies when he pointed out that key terms in the guidelines are not defined: âOMB asked for comment on the definitions of the terms âquality,â âutility,â âobjectivity,â and âintegrityââŠÂ· [but] we are unable to find any definitions of these terms in the proposed guidelinesâŠÂ·. It may seem to the drafters of this proposal that these terms are self-explanatory, but in fact they are not, at least as far as science and regulatory policy are concerned.â It isn't even clear who will be covered by the guidelines, which are silent on the question of applicability to federal grant recipients. âAny attempted federal restriction on dissemination of academic research would be wholly unacceptable in principle and would likely raise First Amendment considerations,â Heinig wrote. The guidelines âshould specifically exclude academic and other non-federal institutions performing research under federal grants.â FASEB's Rich questioned OMB's proposal that government data âbe substantially reproducible upon independent analysis.â âWho will conduct these [independent] studies and who will pay for them?â he wondered. âHow can research studies [that] may have taken place over a period of years and used biological substances be âsubstantially reproducedâ?â Rich also faulted OMB's proposal that federal agencies report the number and nature of complaints regarding their compliance with the guidelines. âThe number of complaints received rather than the validity of the complaints could unfairly impact the reputation of an agency and//or its funded investigators,â he wrote. âA large number of complaints on a scientific matter could simply reflect a controversial issueâŠÂ·rather than the excellence of the science.â The bottom line, Harpel says, is that by seeking to ensure high-quality scientific data, OMB could interrupt ongoing research and leave government agencies and their researchers open to harassment by special interests. That, he states, would âmake it harder to be a scientist.â