ABSTRACT
The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In this paper, we investigate and propose a knowledge graph-based method and implementation of the question-and-answer (QA) system for COVID-19 cases imported from abroad. It mainly analyzes and organizes the knowledge graph construction methods based on knowledge acquisition and visualization. In addition, this paper implements the knowledge graph-based QA system by training term frequency-inverse document frequency (TF-IDF) model and Bidirectional Long Short-Term Memory + Conditional Random Field (Bi-LSTM+CRF) model as well as Cypher query statements using the graph database Neo4j. Finally, the visual intelligent interface of the QA system is designed to meet user requirements and realize the function of accurate QA. © 2021 IEEE.
ABSTRACT
This paper aims at implementing a generic contact tracing framework for efficiently identifying people at risk during any pandemic. It makes use of wireless networks and QR code checkpoints to record close contacts between individuals using their smartphones. It also uses smart cameras to detect special face masks consisting of QR codes in real-time, using the YOLOv5 algorithm. The face masks are then decoded to uniquely identify people wearing them. Moreover, distances are estimated from network signal strengths and the triangle inequality theorem is used to identify close contacts. All information is stored in a graph database for fast analysis. Parallel processing is used to reduce the time taken for identifying and alerting people who were possibly in contact with an infected person. In addition, a web application enables administrators to visualise infection chains and user displacements in an interactive map. The system has been thoroughly evaluated and the results demonstrate that it is highly effective and customisable for any pandemic. Its privacy-oriented aspect also enables a high adoption rate among users. Lastly, the smart camera system enables facemask-driven contact tracing during the Covid-19 pandemic. © 2022 IEEE.