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.
As the frequency and scope of major diseases continue to rise, the need for an efficient early warning and prevention system in public health has become increasingly urgent. This paper addresses the challenges of preventing and predicting high-frequency and sudden-onset diseases, and proposes a blockchain-based solution to construct a trusted data space. The solution integrates blockchain technology with trusted data space construction, effectively addressing the challenges of data sharing and utilization across regions, departments, and business domains for disease warning and prevention. The experiment showed that the solution on-chain TPS(Transactions Per Second) for spatial data is 1318.7, and the single-node QPS(Queries Per Second) is 1999.2. It meets the requirements for handling high-frequency and sudden public health events, and offers certain advantages in data security, sharing efficiency, and privacy protection. Future research will continue to explore the deep integration and extended applications of cross - chain technology, zero - knowledge proof, and other privacy -preserving computing technologies.
Cryptocurrencies have attracted increasing attention worldwide. Cryptocurrency assets are likely to remain a viable choice for the public in long term. In this paper, the modelling of cryptocurrency price is explored in Bitcoin bubbles prior to and during the COVID-19 pandemic. As shown here, it is necessary and possible to understand the dynamics in plausible proxy variables. A similar methodology could be deployed in other situations where market bubbles occur.
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.
Abstract In an era characterized by vast data streams and complex socioeconomic dynamics, the fusion and precise analysis of multi‐sourced intelligence has emerged as a pivotal challenge. To address this, the study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology. Drawing from diverse fields including informatics, computer science, data analytics, and network security, the research adopts an integrative methodology comprising both a comprehensive literature review and systems analysis. Key findings highlight the prowess of AGI‐driven IFNs in enhancing governmental early warning systems for crisis management. These networks underscore a paradigm shift from reactive postevent measures to proactive pre‐event forecasting, thus bolstering the efficacy of governmental responses. Moreover, the decentralized nature of blockchain technology ensures data integrity, fostering trust in interdepartmental data sharing—an essential for efficient crisis management in hierarchical administrative structures. This study accentuates the need for redefining crisis management strategies, emphasizing data‐driven decision‐making and seamless intelligence sharing to ensure optimal outcomes.
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, blockchain’s 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, Óscar G. López
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.
Key messages Grounding practices within the materiality of geography is an important technique for studying the complexity of digital phenomena. The DIGO (Discourses, Infrastructures, Groupings, and Outcomes) framework uses these categories to guide data selection for locating digital phenomenon in material geographies. This article applies the DIGO framework to blockchain (using data about tweets, miners, firms, and ICOs) to show how this digital practice connects to and across material geographies.
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.
Nektarios Aslanidis, Aurelio F. Bariviera, Óscar G. López
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.
Md Shohel Khan, Ajoy Kanti Das, Md. Shohrab Hossain, Husnu S. Narman
The impact of global transformation due to mosquito-borne diseases like dengue is noticeable and according to the World Health Organization, approximately 96 million people are infected by dengue per year. Moreover, the climate of tropical countries, e.g., Bangladesh is highly in favor of dengue. The initiatives taken by different organizations every year are not enough to face the challenges of dengue. To mitigate the effect of dengue, we propose a distributed crowdsourcing framework, the Dengue Tracker System in which the infected patients and the conscious citizen can submit the possible infectious locations. With the submitted data, two separate heatmaps can be generated so that the people and the concerned authority can get ready to face the challenges of dengue. Moreover, the system is deployed on the Ethereum-blockchain to enhance the security of the system. To prevent fake location data, different token generation methods are implemented.
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 product’s 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
We now come to mining a cryptocurrency, which is one of the central pillars of any cryptocurrency implementation. Mining performs three essential tasks:
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 über den intrinsischen Wert von Kryptowährungen und der nachgewiesene Einfluss der Aufmerksamkeit an dem Marktwert verschiedener Vermögenswerte haben uns veranlasst, den Einfluss der Aufmerksamkeit auf den Marktwert von Kryptowährungen zu untersuchen. Als Aufmerksamkeitsindikator haben wir das Volumen von Google-Suchen zu bestimmten der Suchwörter Suchbegriffe genutzt das Suchvolumen von Google-Suchmenge, die auf Schlüsselwörtern basieren, die sich auf unseren Satz von Kryptowährungen einer sehr genauen Granularität beziehen. Unter Verwendung von ARMA und VECM haben wir getestet, ob die Google-Suchmenge die Vorhersage für Kryptowährungspreisentwicklung im Zeitrahmen von 15 Minuten bis zu einem Tag verbessert. Anschließend haben wir den Handel mit dieser Out-of-Sample Prognose simuliert und kamen zu dem Schluss, dass im Fall von häufigem Handel ohne Gebühren, einfache, univariate, autoregressive Modelle besser Ergebnisse produzieren. Unter Vernachlässigung von Gebühren jedoch, verbessert sich durch die Einbeziehung der Variablen für das Google-Suchvolumen das Handelsergebnisse, insbesondere bei stündlichen und täglichen Frequenzen. Unter Verwendung solcher Frequenzen übertraf 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.