Consumers under pandemic anxiety: Suppressors, intensifiers, and resilients

COVID-19 turned the lives of all people across the world upside down. Everyone faced the threat of catching the virus and denial of access to the physical marketplace. For many, it also brought the threat of partial or full unemployment. This trinity of upheaval produced heightened anxiety. The purpose of this article is to understand how consumers coped with anxiety during the pandemic and lockdown periods. We hypothesized that consumers coped with such anxiety by engaging in diverse creative and productive activities, which served as anxiety suppressors. In addition, we hypothesized that one's enduring mind positivity provided resilience and helped consumers mitigate their anxiety. In survey data from a random sample of 550 consumers in the United States, we found support for these hypotheses. Consumers who engaged in voluntary productive activities suffered less anxiety. And consumers with higher resilience levels also felt lower levels of anxiety. In addition, we found that enjoyment of shopping intensified the experience of COVID-19-induced anxiety. The research framework linking this specific set of antecedents to COVID-induced anxiety and its affirmation in this study are new to the literature and therefore offer a notable contribution to it. These findings show two pathways to marketers: Organize and promote voluntary productive activities and offer means for consumers to cultivate personal resilience, on for-profit and not-for-profit platforms. Also, we suggest a future consumer research agenda for when fate again brings us face-to-face with similar or even lesser catastrophes, which, according to scientific forecasters, it sadly but surely will. © 2022 John Wiley & Sons Ltd.

Enhancement of Covid-19 Segmentation Using Machine Learning Analyses of Lung Imaging

The Covid-19 pandemic has caused more then 193 million cases and 4.1 million deaths worldwide as of July 2021. The Fleischner Society reported that Computerized Tomography (CT) is a useful tool for the early identification of Covid-19. Covid-19 disease induces lung changes which can be observed in lung CT predominantly as ground-glass opacification (GGO) and occasional consolidation in the peripheries. Moreover, it was reported that the percentage of lung showing disease correlates with the severity of the disease. Therefore, segmentation of the disease areas in CT images is a logical first step to quantify disease severity. In this paper, we propose g CoviSegNet Enhanced' based on a U-Net with an 813-layer EfficientNetB7 encoder having an attention mechanism to segment the Covid-19 disease area observed in CT images of Covid-19 patients. CoviSegNet Enhanced is an improvement of our previous work g CoviSegNet'. The experiments performed on three public CT datasets and a detailed comparison with recently published work confirms that the proposed CoviNet Enhanced using deep learning approaches is highly effective for Covid-19 segmentation. © 2021 ACM.

CoviSegNet - Covid-19 disease area segmentation using machine learning analyses for lung imaging

The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus - 2 (SARS-CoV-2). Over 175 million cases and 3.8 million deaths were reported worldwide as of June 2021. Covid-19 disease induces lung changes observed in lung Computerized Tomography (CT) predominantly as ground-glass opacification (GGO) with occasional consolidation in the peripheries. It was revealed in some literature that 88% of Covid-19 positive patients' CT scans showed GGO and 32% showed consolidation. Moreover, it was reported that the percentage of the lung showing GGO, and consolidation is tied to disease severity. Thus, segmentation of ground-glass opacities and consolidations in CT images will help to quantify disease severity and assist physicians in disease triage, management, and prognosis. In this paper, we propose CoviSegNet, an enhanced U-Net model to segment these ground-glass opacities and consolidations. The performance of CoviSegNet was evaluated on three public CT datasets. The experimental results show that the proposed CoviSegNet is highly promising. © 2021 IEEE.

CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images

The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus – 2 (SARS-CoV-2). Over 146 million cases and 3.1 million deaths were reported worldwide as of April 27, 2021. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for the early diagnosis of Covid-19. However, this classification involves radiologists’ time and efforts significantly. It is crucial to develop an automated analysis of CT images to save their time and efforts. In this paper, we propose CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose Covid-19 from CT images. We trained and tested the proposed CoviNet using two public datasets with radiologist-labeled CT images. The experimental results show the proposed CoviNet is promising. © 2021 SPIE.

Caution warranted for low-dose radiation therapy for Covid-19 COMMENT

Covid-19 is a morbid respiratory disease that has caused desperate times on a global scale due to the lack of any effective medical treatment. Some in the radiation community are actively proposing low dose radiation therapy (LDRT) for managing the viral pneumonia associated with Covid-19. This commentary provides a rationale for exercising caution against such a decision as the efficacy of LDRT for viral diseases is unknown, while its long-term adverse risks are well known.