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People in United States (US) prisons and jails have been disproportionately impacted by the COVID-19 pandemic. This is due to challenges containing outbreaks in facilities and the high rates of health conditions that increase the risk of adverse outcomes. Vaccination is one strategy to disrupt COVID-19 transmission, but there are many factors impeding vaccination while in custody. We aimed to examine the perspectives of former residents in the Federal Bureau of Prisons (BOP) regarding COVID 19 vaccine hesitancy and acceptance. Between September-October 2021, we conducted semi-structured interviews with 21 recently released individuals who were incarcerated before and during COVID-19 and coded transcripts thematically. We assessed perceptions of the vaccine rollout and factors shaping vaccination uptake in custody and after release. The vaccine was available to seven participants in custody, of whom three were vaccinated. Interviewees had mixed attitudes about how vaccines were distributed, particularly with priority given to staff. Most were reluctant to get vaccinated in custody for varying reasons including observing staff declining to be vaccinated, lack of counseling to address specific questions about safety, and general lack of trust in the carceral system. By contrast, twelve got vaccinated post-release because of greater trust in community health care and stated they would not have done so while incarcerated. For residents in the BOP. COVID-19 vaccination was not simply a binary decision, instead they weighed the costs and benefits with most deciding against getting vaccinated. Institutions of incarceration must address these concerns to increase vaccine uptake as the pandemic continues.(c) 2023 Elsevier Ltd. All rights reserved.
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Objective To explore the occurrence and influencing factors of serum uric acid elevation in patients with coronavirus disease 2019 (COVID-19) treated with favipiravir. Methods Medical records of patients with COVID-19 who were hospitalized in Beijing Ditan Hospital between June 1, 2020 and June 30, 2021 and treated with the 5- or 10-day regimen of favipiravir were collected and retrospectively analyzed. After favipiravir withdrawal, if the elevation in serum uric acid was >=30% of baseline level, it was defined as serum uric acid elevation. Then patients were divided into serum uric acid elevation group and non-serum uric acid elevation group. The clinical characteristics such as gender, age, body mass index, comorbidities, smoking and drinking behavior, COVID-19 grade, favipiravir regimen, and serum uric acid level and renal function before treatment in patients between the 2 groups were compared. Influencing factors of favipiravir-associated serum uric acid elevation was analyzed using multivariate logistic regression method. Results A total of 179 patients were included in the analysis, including 104 (58.1%) males and 75 (41.9%) females, aged from 19 to 70 years with a median age of 43 years. The level of serum uric acid in 179 patients after favipiravir treatment was significantly higher than before [(451+/-119) mumol/L vs. (332+/-94) mumol/L, P<0.001]. The change rate of serum uric acid from baseline level ranged from -57.1% to 157.8% with the median of 38.6%. The elevation in serum uric acid of >= 30% of baseline level occurred in 108 (60.3%) patients. The incidences of serum uric acid elevation in patients treated with 5-day and 10-day regi- mens of favipiravir were 46.8% (36/77) and 70.6% (72/102), respectively, and the difference between them was significant (P=0.001). Multivariate logistic regression analysis showed that body mass index 24.0 to <28.0 kg/m2 (OR=3.109, 95%CI: 1.209-7.994, P=0.019) and 10-day regimen of favipiravir (OR=3.017, 95%CI: 1.526-5.964, P=0.001) were independent risk factors for favipiravir-associated serum uric acid elevation. Conclusions More than half of COVID-19 patients treated with favipiravir can develop serum uric acid elevation. Overweight and 10-day regimen of favipiravir are independent risk factors for serum uric acid elevation in patients.Copyright © 2022 Adverse Drug Reactions Journal.
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Rapidly developing and bring an innovation to market (innovation speed) should have positive effects on performance. Yet innovation and technology management literature also suggests that faster innovation speed may lead to higher innovation failures in some scenarios. We investigate the value of innovation speed in four market scenarios: established markets before and during the COVID pandemic and emergent markets before and during the COVID pandemic. The study findings indicate that the pandemic has major implications for the value of innovation speed. In established markets where customer needs are well defined, rapid innovation increases performance before the pandemic;yet fast innovation speed leads to worst performance during the pandemic. In emergent markets where customer needs are still being formed, before the pandemic, the effect of innovation speed on performance is U-shaped (that is, slow or rapid innovation speed is better than moderate innovation speeds). In contrast, increasing innovation speed always leads to higher performance during the pandemic and innovation is most successful at very fast innovation speed. We present top ten success factors for managing innovation speed before and during the pandemic. The research findings are applicable for the post-pandemic new normal. The study also advances the engineering and technology management literature. © 2023 Elsevier B.V.
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The purpose of this paper is to help better understand the problem of energy poverty;to grasp the research context, evolution trends and research hotspots of energy poverty;and to find clues from research on energy poverty. In this paper, we use the scientific quantitative knowledge graph method and CiteSpace software to analyze 814 studies in the WOS (Web of Science) and CNKI (China National Knowledge Infrastructure) databases, such as a literature characteristic analysis, a core author and research institution network analysis, a research hotspot analysis, research trends and a frontier analysis. The results show that the specific connotations of energy poverty are different between developed countries and developing countries. In developed countries, energy poverty is mainly manifested in the affordability of energy consumption, while in developing countries, energy poverty is manifested in the availability of energy. The causes, impacts and solutions of energy poverty are the focus of CNKI and WOS literature, and their perspectives of the impacts and solutions are relatively consistent. However, in terms of the causes, scholars of WOS discuss the energy supply side and the demand side, while scholars of CNKI mainly analyze the energy demand side. The quantitative evaluation system of energy poverty has not been unified, which restricts the depth and breadth of energy poverty research. Topics such as the expanding scope of research objects;the interaction among energy poverty, the "two-carbon” target and other macro factors;the complex and severe energy poverty situation following the COVID-19 pandemic and the outbreak of the war in Ukraine;and the ways to solve the energy poverty problem in the context of China may become the focus of research in the future. This study provides an overview for researchers who are not familiar with the field of energy poverty, and provides reference and inspiration for future research of scholars in the field of energy poverty research. © 2023 by the authors.
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Corona Virus Disease 2019 (COVID-19) spread globally in early 2020, leading to a new health crisis. Automatic segmentation of lung infections from computed tomography (CT) images provides an important basis for early diagnosis of COVID-19 quickly. In this paper, we propose an effective COVID-19 Lung Infection Segmentation Network (LISNet) based on edge supervision and multi-scale context aggregation. More specifically, an Edge Supervision module is introduced to the feature extraction part to enhance the low contrast between lesions and normal tissues. In addition, the Multi-scale Feature Fusion module is added to enhance the segmentation ability of different scales Lesions. Finally, the Context Aggregation module is used to aggregate high- and low-level features and generate global information. Experiments demonstrate that our method outperforms other state-of-the-art methods on the public COVID-19 CT segmentation dataset. © 2022 IEEE.
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The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.
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The coronavirus disease 2019 (COVID-19) pandemic, which began in 2019, has far-reaching ramifications, including economic losses and health challenges that still affect various parts of the world. During our review, we learned that the entire world is working to stop the spread of the SARS-CoV-2 outbreak. We explore ways that may lower the danger of SARS-CoV-2 contamination and useful strategies to avoid the possibility of SARS-CoV-2 spreading through food. While hygienic protocols are required in the food supply sector, cleaning, disinfection, and the avoidance of cross-contamination across food categories and other related goods at different stages of the manufacturing process remain especially important because the virus can survive for long periods of time on inert materials such as food packaging. Furthermore, personal hygiene (regular washing and disinfection), wearing gloves and using masks, garments, and footwear dedicated to maintaining hygiene provide on-site safety for food sector personnel, supply chain intermediaries, and consumers. Restrictions imposed in response to the pandemic (e.g., closure of physical workplaces, canteens, cafes, restaurants, schools, and childcare institutions), changes in household grocery shopping frequency, individuals' perceived risk of COVID-19, income losses due to the pandemic, and sociodemographic factors are among the factors. The conclusions drawn from this study consider the implications of healthy diets, food system resilience, behavior change, and nutritional imbalance for policymakers and food supply chain participants, as well as the antimicrobial effects of vitamins and nutrients. During a public health crisis, people should eat less, necessitating preventive policies and nutritional advice to deal with this.
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Sex is an important factor that influences disease development, progression, and treatment. In multiple non-reproductive cancers, sex differences in incidence, progression, treatment response, survival, and other clinical outcomes are observed. Overall, males have a 20% higher chance of developing cancer over their lifetime, and experience worse clinical outcomes when compared with females. The NIH recognizes the importance of sex as a biologic variable and addressing sex as a biological variable is now required for all researchers submitting NIH grants. While more researchers are investigating the role of sex differences in cancer, a systematic review that examines the patterns of sex differences in incidence and survival across 15 non-reproductive cancers has not yet been published. We performed a systematic review by searching five databases using keywords and controlled vocabulary terms for each concept of interest and limited to English language. Records were included if it reported sex differences in human adults (18+), addressed incidence, mortality, or survival, at least one of the 15 cancers of interest, and were a cohort, cross-sectional, RCT, or case control study. Covidence was used for screening and two reviewers independently screened each record at title/ and then full text. Two reviewers independently completed data extraction using Microsoft Excel and the Cochrane RoB 2.0, and JBI tools were used for risk of bias assessment. The searches and pilot of the methods are underway. Understanding the role sex-differences play on incidence and survival are important for adding to our understanding of advances in diagnosis and treatment of individuals with cancer.
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Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission. Due to the promising results of deep learning networks in modelling time sequences, we present a temporal-oriented broadcasting residual learning method that achieves efficient computation and high accuracy with a small model size. Based on the EfficientNet architecture, our novel network, named Temporaloriented ResNet (TorNet), constitutes of a broadcasting learning block. The network obtains useful audio-temporal features and higher level embeddings effectively with much less computation than Recurrent Neural Networks (RNNs), typically used to model temporal information. TorNet achieves 72.2% Unweighted Average Recall (UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge COVID-19 cough Sub-Challenge, by this showing competitive results with a higher computational efficiency than other state-of-the-art alternatives. © 2022 IEEE.
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The COVID-19 pandemic, and consequently the difficulty of obtaining feedback on the effectiveness of contamination prevention methods, has caused an increased need to produce a relevant and consistent analysis from collected data. Through Formal Concept Analysis, applying the triadic approach, called Triadic Concept Analysis (TCA), it is possible to evaluate the correlation between prevention measures and the number of contaminated people by performing concept extraction and implication rules. The advantage of using this method is the possibility of correlating the waves, which allows us to explain and understand the evolution of the data over the collection waves, helping us draw a more assertive conclusion from the data analyzed. This paper uses the data collected from the 2020 National Population Survey of Nigeria to depict how Nigerian society's essential and everyday behaviors impacted the evolution of the COVID-19 pandemic in that country. The results obtained from this research can assist governments, and public entities in developing better public policies to combat highly infectious diseases. Furthermore, it provides practical evidence of how TCA can be applied, bringing benefits to different areas and fields of science. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
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The rapid emergence of COVID-19 has become a major public health threat around the world. Although early detection is crucial to reduce its spread, the existing diagnostic methods are still insufficient in bringing the pandemic under control. Thus, more sophisticated systems, able to easily identify the infection from a larger variety of symptoms, such as cough, are urgently needed. Deep learning models can indeed convey numerous signal features relevant to fight against the disease;yet, the performance of state-of-the-art approaches is still severely restricted by the feature information loss typically due to the high number of layers. To mitigate this phenomenon, identifying the most relevant feature areas by drawing into attention mechanisms becomes essential. In this paper, we introduce Spatial Attentive ConvLSTM-RNN (SACRNN), a novel algorithm that is using Convolutional Long-Short Term Memory Recurrent Neural Networks with embedded attention that has the ability to identify the most valuable features. The promising results achieved by the fusion between the proposed model and a conventional Attentive Convolutional Recurrent Neural Network, on the automatic recognition of COVID-19 coughing (73.2 % of Unweighted Average Recall) show the great potential of the presented approach in developing efficient solutions to defeat the pandemic.
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Purpose Logistics capability is an important enabler of supply chain resilience (SCR). However, few studies have analyzed the underlying influence mechanism of logistics capability on SCR in extreme conditions, such as those of the COVID-19 pandemic. The purpose of this study is to increase understanding of the role of logistics capabilities in constituting a resilient supply chain. Design/methodology/approach Drawing upon the dynamic capability perspective and contingency theory, the proposed conceptual framework aims to demonstrate the relationship between a firm's logistics capabilities and SCR. Furthermore, the conceptual framework is illustrated by empirical evidence from a case study of a Chinese manufacturing company, which focuses on extracting practical lessons from the COVID-19 pandemic. Findings The findings suggest that digitalization, innovativeness, and modularization comprise potential mediating pathways for firm logistics capability to affect SCR and government policies, risk management culture, trust and cooperation moderate the effect positively. The potential associations are identified and elucidated by detecting the corresponding strategies and practices of a Chinese manufacturer that performed well amid the COVID-19 pandemic. Practical implications This study provides specific guidelines for logistics managers to enhance SCR during the COVID-19 pandemic. Seeing SCR as a dynamic capability, the framework is also instructive for manufacturers, supply chain members, and policymakers to achieve the sustained competitive advantage of supply chains. Originality/value The findings expand the understanding of enhancing SCR in a logistics approach. The empirical validation of propositions in the case study reveals a new vista for research on SCR.
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Objective To explore the occurrence and influencing factors of serum uric acid elevation in patients with coronavirus disease 2019 (COVID⁃19) treated with favipiravir. Methods Medical records of patients with COVID⁃19 who were hospitalized in Beijing Ditan Hospital between June 1, 2020 and June 30, 2021 and treated with the 5- or 10-day regimen of favipiravir were collected and retrospectively analyzed. After favipiravir withdrawal, if the elevation in serum uric acid was ≥30% of baseline level, it was defined as serum uric acid elevation. Then patients were divided into serum uric acid elevation group and non-serum uric acid elevation group. The clinical characteristics such as gender, age, body mass index, comorbidities, smoking and drinking behavior, COVID⁃19 grade, favipiravir regimen, and serum uric acid level and renal function before treatment in patients between the 2 groups were compared. Influencing factors of favipiravir⁃associated serum uric acid elevation was analyzed using multivariate logistic regression method. Results A total of 179 patients were included in the analysis, including 104 (58.1%) males and 75 (41.9%) females, aged from 19 to 70 years with a median age of 43 years. The level of serum uric acid in 179 patients after favipiravir treatment was significantly higher than before [(451±119) μmol/L vs. (332±94) μmol/L, P<0.001]. The change rate of serum uric acid from baseline level ranged from -57.1% to 157.8% with the median of 38.6%. The elevation in serum uric acid of ≥ 30% of baseline level occurred in 108 (60.3%) patients. The incidences of serum uric acid elevation in patients treated with 5-day and 10-day regi⁃ mens of favipiravir were 46.8% (36/77) and 70.6% (72/102), respectively, and the difference between them was significant (P=0.001). Multivariate logistic regression analysis showed that body mass index 24.0 to <28.0 kg/m2 (OR=3.109, 95%CI: 1.209-7.994, P=0.019) and 10-day regimen of favipiravir (OR=3.017, 95%CI: 1.526-5.964, P=0.001) were independent risk factors for favipiravir⁃associated serum uric acid elevation. Conclusions More than half of COVID⁃19 patients treated with favipiravir can develop serum uric acid elevation. Overweight and 10-day regimen of favipiravir are independent risk factors for serum uric acid elevation in patients. © 2022 Adverse Drug Reactions Journal.
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Due to the pandemic caused by COVID-19, the demand for face masks is soaring and has often caused a shortage. The aim of this study was to evaluate the effect of ultraviolet (UV) and drying treatments on microbial contaminants in facial masks. To conduct this study, standard procedures were designed to develop samples contaminated by the control bacteria Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The contamination level of the standard samples was approximately 6.30 × 106 CFU/ml, and the UV light treatment was performed 1, 3, 5, and 7 times. To evaluate the effect of the UV and drying treatments, the masks were first treated with UV 1, 2, and 3 times, followed by the drying process. As a result, the mask contaminated with E. coli and P. aeruginosa showed a bacterial rate of approximately 99.9% after 1 UV irradiation, and in the case of the S. aureus-contaminated mask, it exhibited a bactericidal rate of approximately 99.9% after 7 UV irradiations. However, when the drying process was included after UV irradiation, all the samples contaminated with E. coli, S. aureus, and P. aeruginosa showed a bactericidal rate of 99.9% or more. The results of this study suggest that UV and drying treatments can effectively reduce the bacterial contaminants in facial masks. In addition, these results provide fundamental data and appropriate sterilization methods for reusing masks.
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Advanced metering infrastructure (AMI) is an integrated system of smart meters, communication networks, and data management systems. The AMI allows the automatic and remote measurement and monitoring of energy consumption. It also provides important information for the management of peak demand and energy consumption and costs. Pohang University of Science Technology (POSTECH) has developed its own AMI and an IT platform called Open Innovation Big Data Center (OIBC) to store and share various data collected in the campus. In this work, we describe the AMI and the OIBC platform equipped with various sensors and systems for measuring, storing, calling, and monitoring data. Data are collected from seven buildings with different characteristics. We installed 266 sensors at the buildings, including 188 EnerTalk and Biz, 18 plugin, and 60 high-sampling sensors. The sensors collect electricity consumption data in real time, and users can visualize and download the data through the OIBC platform. In this work, we present analysis results of the collected data. The results show that the amounts of electricity consumed by campus buildings are different depending on various factors, including building size, occupant type and their behaviors, and building use. We also compare the amounts of electricity consumed before and after the COVID-19 outbreak. The information extracted can be used to improve the satisfaction of students and faculty as well as the efficiency of electricity management. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Recently, "environment-to-human" transmission has become a new pattern in the spread of COVID-19, and novel coronavirus variants with increased infectivity have appeared in many countries. With considering the "environment-to-human" transmission and virus mutations of the novel coronavirus, the SEIQR model of infectious disease dynamics was established to simulate the development trend of the epidemic. The results showed that, the increased infectivity of virus variants and "environment-to-human" transmission positively affects the spread and speed of COVID-19 epidemic, in which the virus variants had a more significant impact, and the "environment-to-human" transmission will promote the outbreak time of the epidemic to be greatly advanced. For the virus variants with higher infectivity, elevating the intensity of interventions has a more remarkable effect on controlling the spread of COVID-19 variants. © 2021, The Editorial Department of Complex Systems and Complexity Science. All right reserved.
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Drug repurposing may be a pivotal means of fulfilling urgent needs for treatment of the novel coronavirus disease 2019 (COVID-19), but current studies on drug repurposing for COVID-19 seem to show a lack of consensus in their drug candidate focus. Using bibliometric methods in a non-expert perspective, in a review of 34 published articles on the COVID-19 and drug-repurposing, we investigated obvious and less obvious points of consensus on drug candidates. To establish these two types of consensus, we first implemented document clustering. Within a set of five clustered papers, we established an obvious consensus, relying solely on the occurrence of entities by using term frequency and inverse document frequency and a comparison of mentioned drugs, finding that remdesivir and chloroquine were discussed with a certain degree of agreement. For the less obvious consensus, we created a drug entity co-occurrence network to establish low-high centrality combinations to probe the crucial drugs found in article clustering that are not plainly apparent through the mere counting of the occurrence of drug entities occurrences. Lopinavir emerged as having possibly potent effects in spite of underuse, while the mainstream of studies focus more on drugs such as chloroquine that enjoy explicit consent. Using an entitymetrics perspective, we expect that our research will support investigations of drug repurposing, expediting the process of establishing treatment for COVID-19.
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With the continuous development of sensor technology, computer technology, artificial intelligence and other advanced technologies, there are more and more researches on trajectory tracking and detection technology, which have been widely used in urban planning, traffic management, safety control and other aspects. Trajectory tracking and detection has always been the focus of research by experts and scholars. The purpose of this study is to track and detect the spatial trajectory of the infected person under the current new crown virus epidemic, to timely and accurately understand the itinerary of the new crown virus infected person and to find out all the suspected contacts that the infected person may come into contact with. The current epidemic situation in various countries has made a certain contribution. © 2021 ACM.