Hao Xu, Lei Zhang, Oluwakayode Onireti, Yang Fang · 6 authors
The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.
Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.
The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.
The Fractional Gray Lotka-Volterra Model (FGLVM) is introduced and used for modeling the transaction counts of three cryptocurrencies, namely, Bitcoin, Litecoin, and Ripple. The 2-dimensional study is on Bitcoin and Litecoin, while the 3-dimensional study is on Bitcoin, Litecoin, and Ripple. Dataset from 28 April 2013 to 10 February 2018 provides forecasting values for Bitcoin and Litecoin through the 2-dimensional FGLVM study, while dataset from 7 August 2013 to 10 February 2018 provides forecasting values of Bitcoin, Litecoin, and Ripple through the 3-dimensional FGLVM study. Forecasting values of cryptocurrencies for the n-dimensional FGLVM study, n={2,3} along 100 days of study time, are displayed. The graph and Lyapunov exponents of the 2-dimensional Lotka-Volterra system using the results of FGLVM reveal that the system is a chaotic dynamical system, while the 3-dimensional Lotka-Volterra system displays parabolic patterns in spite of the chaos indicated by the Lyapunov exponents. The mean absolute percentage error indicates that 2-dimensional FGLVM has a good accuracy for the overall forecasting values of Bitcoin and a reasonable accuracy for the last 300 forecasting values of Litecoin, while the 3-dimensional FGLVM has a good accuracy for the overall forecasting values of Bitcoin and a reasonable accuracy for the last 300 forecasting values of both Litecoin and Ripple. Both 2- and 3-dimensional FGLVM analyses evoke a future constant trend in transacting Bitcoin and a future decreasing trend in transacting Litecoin and Ripple. Bitcoin will keep relatively higher transaction counts, with Litecoin transaction counts everywhere superior to that of Ripple.
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 company’s 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