Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials

Addressing conflicts of interest regarding the vaccine in infectious disease outbreaks based on good governance for health approach: a policy brief

BACKGROUND

Infectious disease outbreaks pose a significant threat to public health, and achieving herd immunity highlights the importance of addressing conflicts of interest (COI) in vaccine development and policy-making. This policy brief aims to present policy options that address COI regarding vaccines in infectious disease outbreaks, based on good governance for health approach.

METHODS

Our study used a scoping review methodology. We conducted a systematic search, which led to identifying 43 eligible articles. A qualitative approach (i.e., content analysis) was employed for data analysis, using "ATLAS.ti 9" software. The primary results underwent a process of cleaning, categorisation, and subsequent discussion in three sessions with the research team.

RESULTS

Relationships between theindustry and "government/policymakers" as well as "academic institutions/researchers" are prominent origins of COI regarding the vaccine in infectious disease outbreaks. To address this issue, we present nine policy options that target both the root cause of the problem and the adoption of good governance for health approach.

CONCLUSIONS

The key principles of good governance for health, including, "Transparency", "The Rule of Law", "Effectiveness", "Efficiency", "Participation", "Consensus Orientation", "Equality", "Responsibility", "Responsiveness" and "Accountability" must be taken into account when formulating policy options to address COI regarding the vaccine in infectious disease outbreaks. The effectiveness of the policy options outlined in this policy brief should be assessed in practical contexts, as this evaluation may uncover the need for revisions.

BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet

A vehicular ad hoc network (VANET) is a technique that uses vehicles with the ability to sense data from the environment and use it for their safety measures. Flooding is a commonly used term used for sending network packets. VANET may cause redundancy, delay, collision, and the incorrect receipt of the messages to their destination. Weather information is one of the most important types of information used for network control and provides an enhanced version of the network simulation environments. The network traffic delay and packet losses are the main problems identified inside the network. In this research, we propose a routing protocol which can transmit the weather forecasting information on demand based on source vehicle to destination vehicles, with the minimum number of hop counts, and provide significant control over network performance parameters. We propose a BBSF-based routing approach. The proposed technique effectively enhances the routing information and provides the secure and reliable service delivery of the network performance. The results taken from the network are based on hop count, network latency, network overhead, and packet delivery ratio. The results effectively show that the proposed technique is reliable in reducing the network latency, and that the hop count is minimized when transferring the weather information.

A reliable vaccine tracking and monitoring system for health clinics using blockchain

Vaccines are delicate biological substances that gradually become inactive over time and must be kept under a recommended temperature range of 2-8 °C for both short and long-term storage. Exposure to heat or freezing temperatures can highly affect the immunological properties of these vaccines and make them completely ineffective. Research shows that vaccine exposure to temperatures outside the recommended range is 33% in developed countries and 37.1% in developing countries. In practice, vaccines are stored in refrigerators, while thermometers and data loggers are used to record and monitor temperatures. However, traditional systems are unreliable due to lack of battery backup, human error, periodic logging of temperatures, etc. Therefore, an effective and reliable vaccine tracking and monitoring system is urgently needed. This paper proposes a blockchain-based, smart contract enabled solution that ensures an enhanced level of security, transparency, and traceability of stored vaccines in a health clinic, and enables the complete history of every vaccine to be checked from the day the vaccine is received by the health clinic to the date it is used or expires. We also formally analyze the resiliency of the proposed system against several attacks and compare the system with existing blockchain and non-blockchain-based solutions.

Improving the distribution of covid-19 vaccines using the blockchain technology: the Italian case study

Vaccination is one of the most effective ways to prevent and control the outbreak of infectious diseases. The vaccine supply chain differs from the traditional supply chains because of the perishability of the products, which need strict transport and warehousing conditions to guarantee the health and safety of people. In addition, in case of pandemics, the big amount of doses requested for the implementation of a mass vaccination campaign forces governments to design a proper logistic network and plan a rapid and efficient distribution of vaccines. This paper studies the organization of allocation and distribution of the covid-19 vaccines in Italy. The main criticalities in managing the vaccine supply chain have been identified and, because of its peculiarities, the blockchain has been considered a suitable technology to solve them. A simulation model has been developed to reproduce the current distribution of vaccines in Italy, and a future scenario with blockchain has been studied. The findings show that it is possible to improve the performance of the vaccine supply chain and make it more resilient by implementing the blockchain technology.

Vaccine supply chain coordination using blockchain and artificial intelligence technologies

Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.

A Traceable Vaccine Supply Management System

Everyone should be vaccinated, but the eligibility and safety of the vaccine are always overlooked by most people. The outbreak of COVID-19 has led many countries to intensify the development and production of the COVID-19 vaccine. and some countries have even required universal vaccination against this epidemic. However, such popularization of vaccination has also exposed various flaws in vaccine management that existed in the past, and vaccinators have become more concerned about the effectiveness of their vaccinations. In this paper, we propose a blockchain-based traceable vaccine management system. First, the system uses smart contracts to store the records generated during the whole process, from vaccine production to vaccination. Second, the proposed scheme uses the Edwards-curve digital signature algorithm (EdDSA) to guarantee the security and integrity of these data. Third, the system participants can access the corresponding data according to their authority to ensure the transparency of the whole system operation process. Finally, this paper will also conduct a security analysis of the whole system to ensure that the system can resist potential attacks by criminals.

Asseri A, Binyamin SS, Al-Rabia MW. Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

Do Vaccination Attitudes Mediate the Link between Critical Consciousness and COVID-19 Vaccination Behaviour? A Cross-Sectional Study

Critical consciousness development represents a building block in the formation of health-related attitudes and behaviours. One of the most studied health-related behaviours in the previous year is COVID-19 vaccination behaviour. The present study examines critical consciousness, COVID-19 vaccination attitudes and vaccination behaviour in a consistent sample of young adults (N = 1185). Participants were residents of Romania, aged 18 to 41 years. The Critical Consciousness Scale—Short Form (CCS-S) and the Vaccination Attitudes Examination (VAX) Scale were used. The results demonstrate the mediating role of two types of vaccination attitude—lack of confidence in the promoted vaccine benefits and worries about unforeseen effects—in the relationship between critical reflection and vaccination behaviour. Communication is fundamental in an initiative aimed at changing behaviour. When shaping a health-related campaign, communication and dialogue must be horizontal and characterised by empathy and mutual recognition to determine favourable health-related behaviour.