Factors associated with contact tracing compliance among communities of color in the first year of the COVID-19 pandemic.

RATIONALE: The disproportionate impact of COVID-19 on communities of color has raised questions about the unique experiences within these communities not only in terms of becoming infected with COVID-19 but also mitigating its spread. The utility of contact tracing for managing community spread and supporting economic reopening is contingent upon, in part, compliance with contact tracer requests. OBJECTIVE: We investigated how trust in and knowledge of contact tracers influence intentions to comply with tracing requests and whether or not these relationships and associated antecedent factors differ between communities of color. METHOD: Data were collected from a U.S. sample of 533 survey respondents from Fall (2020) to Spring 2021. Multi-group SEM tested quantitative study hypotheses separately for Black, AAPI, Latinx, and White sub-samples. Qualitative data were collected via open-ended questions to inform the roles of trust and knowledge in contact tracing compliance. RESULTS: Trust in contact tracers was associated with increased intentions to comply with tracing requests and significantly mediated the positive relationship between trust in healthcare professionals and government health officials with compliance intentions. Yet, the indirect effects of trust in government health officials on compliance intentions were significantly weaker for the Black, Latinx, and AAPI samples compared to Whites, suggesting this strategy for increasing compliance may not be as effective among communities of color. Health literacy and contact tracing knowledge played a more limited role in predicting compliance intentions directly or indirectly, and one that was inconsistent across racial groups. Qualitative results reinforce the importance of trust relative to knowledge for increasing tracing compliance intentions. CONCLUSIONS: Building trust in contact tracers, more so than increasing knowledge, may be key to encouraging contact tracing compliance. Differences among communities of color and between these communities and Whites inform the policy recommendations provided for improving contact tracing success.

PROMPT: Process Mining and Paravector Tensor-Based Physical Health Monitoring Framework

The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems, very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT)-based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis of a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world. © 2001-2012 IEEE.

PROMPT: PROcess Mining and Paravector Tensor based Physical Health Monitoring Framework

The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems but very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT) based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis on a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world. IEEE

SMEs and digital transformation during a crisis: The emergence of resilience as a second-order dynamic capability in an entrepreneurial ecosystem

In this research, we examine how small and medium enterprises (SMEs) build their resilience capability during a crisis, through the adoption of digital technologies. Utilizing a qualitative approach grounded in case studies of eight entrepreneurs from India who had to radically change their business models and operations during the COVID-19 pandemic, we develop a multilevel model of resilience capability: at the micro (entrepreneur), meso (organizational), and macro (entrepreneurial ecosystem) levels. In developing resilience, the SMEs alternate their focus: from concentrating on the core to moving toward the periphery of their organizational boundaries, highlighting a shifting play of the three first-order dynamic capabilities of sensing, seizing, and transforming. By affording SMEs an opportunity to transform themselves by embracing digital technologies, the crisis leads to the emergence of resilience capability as a second-order dynamic capability.

STRENUOUS: Edge-Line Computing, AI and IIoT enabled GPS Spatiotemporal data-based Meta-transmission Healthcare Ecosystem for Virus Outbreaks Discovery

COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims a HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. Proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The Proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus. IEEE

Crisis and arbitrage opportunities: The role of causation, effectuation and entrepreneurial learning

This article examines the process by which entrepreneurs identify and work with an arbitrage opportunity emerging from an episodic crisis. Although prior research has investigated the role of entrepreneurial characteristics and context on opportunity development, the specific manner in which these factors emerge in the course of opportunity development during a crisis remain underexplored. By adopting a qualitative approach grounded in case studies of eight entrepreneurs in the US distillery industry, this article addresses that gap by examining the process of arbitrage opportunity development during COVID-19. Our study reveals the primacy of both causation and effectuation-based entrepreneurial decision logics and the role of double-loop learning, as entrepreneurs interact with the time-compressed duration of the arbitrage opportunity. Implications and insights for entrepreneurs, researchers and policymakers are discussed.