Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PD

The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PD

Autonomous and Adaptive Learning Architecture Framework for Smart Cities

The context of smart cities should really be anchored onto two key attributes. First is the ability for a city to learn adaptively with the aid of machine learning (ML) or artificial intelligence (AI) and second is the ability for a city to sustain operations autonomously without any human intervention. While Internet of Things (IoT) is seen as an enabler by making all devices connect to a network that communicates with one another with minimal human interference; however, critical problems such as sewer management, health, parking woes, traffic congestion, pollution, waste management, and noise are not fully being addressed. In this paper, we discussed the most recent literature for smart city initiatives across the globe including the comprehensive Alcatel–Lucent study in 2011 and proposed an overarching autonomous learning city baseline and target architecture with specific functionalities for each layer

Digital know-how and FinTech readiness

The finance and accounting domains today require institutions of higher learning to produce competent graduates who are familiar with digital soft skills. These include Blockchain, e-audit, big data, cryptocurrencies, auditing on the cloud, softbot counseling, robotics in business process automation, peer-to-peer transactions, application of artificial intelligence in treasury-related work, and crowdfunding. The voguishness of technology usage in the digital economy today has become an impelling force to the accounting and finance professions. Industry experts argue that the lack of FinTech skills will adversely impact recruitment. FinTech-enabled platforms create disruption to the accounting workforce as newer emerging business models and processes are slowly rising across banking, finance, audit, and integrated reporting. In this article, we present the top 10 skills needed in terms of FinTech readiness that should be included in existing curriculums offered at the tertiary level from expert opinion gathered by relevant stakeholders

Autonomous and Adaptive Learning Architecture Framework for Smart Cities

The context of smart cities should really be anchored onto two key attributes. First is the ability for a city to learn adaptively with the aid of machine learning (ML) or artificial intelligence (AI) and second is the ability for a city to sustain operations autonomously without any human intervention. While Internet of Things (IoT) is seen as an enabler by making all devices connect to a network that communicates with one another with minimal human interference; however, critical problems such as sewer management, health, parking woes, traffic congestion, pollution, waste management, and noise are not fully being addressed. In this paper, we discussed the most recent literature for smart city initiatives across the globe including the comprehensive Alcatel–Lucent study in 2011 and proposed an overarching autonomous learning city baseline and target architecture with specific functionalities for each laye

Contextualization of Smart Healthcare: A Systematic Review

Healthcare today is reliant substantially on innovations in Information Communications Technologies (ICT) that include Internet of Things (IoT) and Artificial Intelligence (AI). Coined as Smart Healthcare, applications for integrated health services has been an integral part of focus for the Smart City initiative globally. Nevertheless, contextualization for a complete definition for Smart Healthcare is still very much obscure. In view of this gap, we conducted a comprehensive examination on the definition of Smart Healthcare via a meta-analysis procedure through systematic review of literatures from: (1) Definitions proposed from literary sources and (2) Smart City frameworks. The goal of this paper is not only to provide a closure for Smart Healthcare but also to provide a unified guideline for developers as well as governments in achieving their Smart Healthcare initiatives

An overarching architectural framework for smart cities

The journey toward realizing a “smart city” leads back to one path that many learned people argue is “the best solution for a smart city.” This solution embraces the Internet of Things (IoT) in every aspect of the city as much as possible. The debates on IoT as a solution have been ongoing, and not many have come up with an answer that differs from the mainstream. This research intends to find out whether IoT is a viable solution and if other potential options are better. This study was based on a qualitative approach that began with 1) a survey of literature, 2) preliminary results highlighting that IoT is not a viable solution, and 3) with IoT alone, cities are deemed partially smart. Findings indicate that we require two crucial components, i.e., being autonomous and self-learning in order to achieve a smart city

Development of Bluetooth Enabled (BLE) IOT Digital Surveillance Contact Tracer and Smart Contact Tracing Index

The world has ingresses into Smart Healthcare era in which, it is a construct under the context of Smart City, in making solutions in healthcare to be smart. Even with extensive advancements in smart healthcare solutions, the world especially Malaysia were caught unprepared for smart contact tracing solutions in facing Covid-19 pandemic. Although four main contact tracing solutions were developed by Malaysia and implemented nationwide, there is still no solution yet to made available for the front liners in preventing asymptomatic human-to-human Covid-19 infection. Thus, this study will serve as an applied research in closing the real-life problem stated, by developing Bluetooth Enabled (BLE) IOT digital surveillance contact tracer up to a stage that it is deployment-ready. Not just that, a secondary exploratory research in developing Smart Contact Tracing Index which is non-existent at the moment, also will be conducted. Purpose – The purpose of this study is to develop Bluetooth Enabled (BLE) IOT digital surveillance contact and to develop Smart Contact Tracing Index

A New Approach on Covid-19 Contact Tracing – Employment of Low Calibrated Transmission Power & Signal Captures in BLE

Covid-19 pandemic has forced countries to adopt contact tracing comprehensively in order to restrain the highly infectious virus from further advancement. In this case, Bluetooth Low Energy (BLE) has been widely used utilizing Received Signal Strength Indicator (RSSI) for Close Contact Identification (CCI). Nevertheless, big portion of the available solutions are not able to follow to the rules provided by Centers for Disease Control (CDC) and Prevention which are: (1) Distance requirement of within 6-feet (~2 meters) and (2) Duration of no less than 15-minutes for CCI. Purpose - The purpose of the research is to develop and test a new approach on contact tracing for Covid19 using low calibrated transmission power and signal scans in Bluetooth Low Energy (BLE)

Towards Optimization of Patients’ Turnaround Time using Bluetooth Low Energy Based Solutions

Smart Healthcare can use the Internet of Things (IoT) to broaden the reach of digital healthcare by collecting patient data remotely using sensors. This can reduce Patient Turnaround Time (PTAT) and enable high-quality care to be provided. PTAT is the length of time from when a patient arrives at the hospital until they are allowed to return home. Malaysia's Ministry of Health claimed in 2016 that healthcare at government hospitals continues to encounter issues in providing high-quality care to patients, particularly in terms of the PTAT of patients who receive treatment versus those who are sent home without treatment. In this paper, we propose a Bluetooth Low Energy-based solution that optimizes PTAT using low calibrated transmission power, allowing hospitals to enable Real-time Patient Localization and Patient Movement Monitoring. The RSSI value is used to calculate the distance between a wearable device and the Access Points (AP) situated throughout the facility. When a patient passes an AP, data such as the wearable device name and RSSI value are taken and saved in a database, to determine the patient's location. A proof of concept was conducted using three AP points and 8 wearable devices to gauge distance measurement