ABSTRACT
There are still outbreaks of COVID-19 across the world. Ships increase the risk of worldwide transmission of the virus. Close contact tracing remains as an effective method of reducing the risk of virus transmission. Therefore, close contact tracing in ship environments becomes a research topic. Exposure Notifications API (Application Programming Interface) can be used to determine the encountered location points of close contacts on ships. Location points of close contact are estimated by the encountered location points. Risky areas in ships can be calculated based on the encountered location points. The tracking of close contacts is possible with Bluetooth technology without the Internet. The Bluetooth signal can be used to judge the proximity among detecting devices by using the feature that Bluetooth has a strong signal at close range. This Bluetooth feature makes it possible to trace close contacts in ship environments. In this paper, we propose a method for close contact tracing and showing the risky area in a ship environment by combining beacon and Exposure Notification API using Bluetooth technology. This method does not require an Internet connection for tracing close contacts and can protect the personal information of close contacts. Copyright © 2023 KSII.
ABSTRACT
Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.
ABSTRACT
Contact tracing is the process of identifying people who came into contact with an infected person ('case') and collecting information about these contacts. Contact tracing is an essential part of public health infrastructure and slows down the spread of infectious diseases. Existing contact tracing methods are extremely time and labor intensive due to their reliance on manually interviewing cases, contacts, and locations visited by cases. Additionally, complex privacy regulations mean that contact tracers must be extensively trained to avoid improper data sharing. App-based contact tracing, a proposed solution to these problems, has not been widely adopted by the general public due to privacy and security concerns. We develop a secure, semantically rich framework for automating the contact tracing process. This framework includes a novel, flexible ontology for contact tracing and is based on a semi-federated data-as-a-service architecture that automates contact tracing operations. Our framework supports security and privacy through situation-aware access control, where distributed query rewriting and semantic reasoning are used to automatically add situation based constraints to protect data. In this paper, we present our framework along with the validation of our system via common use cases extracted from CDC guidelines on COVID-19 contact tracing. © 2021 IEEE.