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1.
Energy Research & Social Science ; 85:102401, 2022.
Article in English | ScienceDirect | ID: covidwho-1556979

ABSTRACT

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

2.
European Journal of Inflammation (Sage Publications, Ltd.) ; : 1-13, 2021.
Article in English | Academic Search Complete | ID: covidwho-1136205

ABSTRACT

COVID-19 is spreading exponentially. In order to optimize medical resources allocation and reduce mortality, biomarkers are needed to differentiate between COVID-19 patients with or without severe diseases early as possible. We searched Ovid MEDLINE(R), Ovid EMBASE, CNKI, Wanfang, VIP databases, the Cochrane Library, and medRxiv for primary articles in English or Chinese up to March 30, 2020 to systematically evaluate the risk factors for severe patients in China. Mean difference or standardize mean difference and odds ratio with 95% confidence intervals were performed by random-effect or fixed models in cases of significant heterogeneity between studies. We used I 2 to evaluate the magnitude of heterogeneity. A total of 54 articles involving about 7000 patients were eligible for this meta-analysis. In total, 52 of 67 parameters between severe and non-severe cases were significantly different. Elderly male patients with comorbidities including hypertension, diabetes, chronic obstructive pulmonary disease (COPD) cardiovascular disease, cerebrovascular disease, chronic kidney disease, or cancer were more common in severe COVID-19 patients. Regarding the clinical manifestations on admission, fever, cough, expectoration, dyspnea, chest distress, fatigue, headache, chills, anorexia, or abdominal pain were more prevalent in severe COVID-19 patients. The results of the clinical examination showed that high C-reactive protein (CRP), high lactate dehydrogenase (LDH), high D-dimer, and decreased T lymphocytes cells subsets, decreased lymphocyte may help clinicians predict the progression of severe illness in patients with COVID-19. Our findings will be conducive for clinician to stratify the COVID-19 patients to reduce mortality under the relative shortage of medical resources. [ABSTRACT FROM AUTHOR] Copyright of European Journal of Inflammation (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

3.
Academic Journal of Second Military Medical University ; 41(3):303-306, 2020.
Article in Chinese | CAB Abstracts | ID: covidwho-829625

ABSTRACT

Coronavirus disease 2019 (COVID-19) was first reported in late December 2019, and then erupted in China. COVID-19 is characterized by strong infectivity and a high mortality rate. The public and medical staff are under great psychological pressure. Scholars at home and abroad have carried out researches on mental health during the outbreak of COVID-19. This article summarizes the current researches on mental health related to COVID-19 from three aspects: mental health policy, mental intervention measures and mental health of key population.

4.
IEEE J Biomed Health Inform ; 24(10): 2787-2797, 2020 10.
Article in English | MEDLINE | ID: covidwho-724919

ABSTRACT

Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/diagnosis , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , Supervised Machine Learning , Tomography, X-Ray Computed/statistics & numerical data , Algorithms , COVID-19 , COVID-19 Testing , Cohort Studies , Computational Biology , Coronavirus Infections/classification , Deep Learning , Diagnostic Errors/statistics & numerical data , Humans , Neural Networks, Computer , Pandemics/classification , Pneumonia, Viral/classification , Retrospective Studies , SARS-CoV-2
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