A fuzzy sustainable model for COVID-19 medical waste supply chain network

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. © 2023, Crown.

Universal health information is essential for universal health coverage.

Universal access to health information is a human right and essential to achieving universal health coverage and the other health-related targets of the sustainable development goals. The COVID-19 pandemic has further highlighted the importance of trustworthy sources of health information that are accessible to all people, easily understood and acted on. WHO has developed Your life, your health: Tips and information for health and wellbeing, as a new digital resource for the general public which makes trustworthy health information understandable, accessible and actionable. It provides basic information on important topics, skills and rights related to health and well-being. For those who want to learn more, in-depth information can be accessed through links to WHO videos, infographics and fact sheets. Towards ensuring access to universal health information, this resource was developed using a structured method to: (1) synthesise evidence-based guidance, prioritising public-oriented content, including related rights and skills; (2) develop messages and graphics to be accessible, understandable and actionable for all people based on health literacy principles; (3) engage with experts and other stakeholders to refine messages and message delivery; (4) build a digital resource and test content to obtain feedback from a range of potential users and (5) adapt and co-develop the resource based on feedback and new evidence going forward. As with all WHO global information resources, Your life, your health can be adapted to different contexts. We invite feedback on how the resource can be used, refined and further co-developed to meet people's health information needs.

Predicting COVID-19 from Chest X-ray Images using a New Deep Learning Architecture

The spread of coronavirus disease in late 2019 caused huge damage to human lives and forced a chaos in health care systems around the globe. Early diagnosis of this disease can help separate patients from healthy people. Therefore, precise COVID-19 detection is necessary to prevent the spread of this virus. Many artificial intelligent technologies for example deep learning models have been applied successfully for this task by employing chest X-ray images. In this paper, we propose to classify chest X-ray images using a new end-To-end convolutional neural network model. This new model consists of six convolutional blocks. Each block consists of one convolutional layer, one ReLU layer, and one max-pooling layer. The new model was applied on a challenging imbalanced COVID19 dataset of 5000 images, divided into two classes, COVID and Non-COVID. In experiments, the input image is first resized to 256×256×3 before being fed to the model. Two metrics were used to test our new model: sensitivity and specificity. A sensitivity rate of 97% was achieved along with a specificity rate of 99.32%. These results are promising when compared to other deep learning models applied on the same dataset. © 2022 IEEE.

Densely Connected Neural Network and Permutation Entropy in the Early Diagnostic in COVID Patients

The COVID-19 pandemic has been characterized by the high number of infected cases due to its rapid spread around the world, with more than 6 million of deaths. Given that we are all at risk of acquiring this disease and that vaccines do not completely stop its spread, it is necessary to continue proposing tools that help mitigate it. This is the reason why it is ideal to develop a method for early detection of the disease, for which this work uses the Stanford University database to classify patients with SARS-CoV-2, also commonly called as COVID-19, and healthy ones. In order to do that we used a densely connected neural network on a total of 77 statistical features, including permutation entropy, that were contrasted from two different time windows, extracted from the heart rate of 24 COVID patients and 24 healthy people. The results of the classification process reached an accuracy of 86.67% and 100% of precision with the additional parameters of recall and F1-score being 80% and 88.89% respectively. Finally, from the ROC curve for this classification model it could be calculated an AUC of 0.982. © 2022 Creative Commons.

Examining Social Determinants of Health During a Pandemic: Clinical Application of Z Codes Before and During COVID-19.

Recognition of the impact of social determinants of health (SDoH) on healthcare outcomes, healthcare service utilization, and population health has prompted a global shift in focus to patient social needs and lived experiences in assessment and treatment. The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) provides a list of non-billable "Z codes" specific to SDoH for use in electronic health records. Using population-level analysis, this study aims to examine clinical application of Z codes in South Carolina before and during the COVID-19 pandemic. The study population consists of South Carolina residents who had a healthcare visit and had their COVID-19 test result reported to the state's Department of Health and Environmental Control before January 14, 2021. Of the 1,190,531 individuals in the overall sample, Z codes were used only for 14,665 (1.23%) of the patients, including 2,536 (0.97%) COVID-positive patients and 12,129 (1.30%) COVID-negative patients. Compared with hospitals that did not use Z codes, those that did were significantly more likely to have higher bed capacity (p = 0.017) and to be teaching hospitals (p = 0.03), although this was significant only among COVID-19 positive individuals. Those at inpatient visits were most likely to receive Z codes (OR: 5.26; 95% CI: 5.14, 5.38; p < 0.0001) compared to those at outpatient visits (OR: 0.07; 95%CI: 0.06, 0.07; p < 0.0001). There was a slight increase of Z code use from 2019 to 2020 (OR: 1.33, 95% CI: 1.30, 1.36; p < 0.0001), which was still significant when stratified by facility type across time. As one of the first studies to examine Z code use among a large patient population, findings clearly indicate underutilization by providers. Additional study is needed to understand the potentially long-lasting health effects related to SDoH among underserved populations.

Semi-automatic Segmentation of COVID-19 Infection in Lung CT Scans

As the prevalence of COVID-19, concerns about the treatment of the disease and its impact on communities’ future have increased sharply. The best way to prevent the spread of COVID-19 disease is to quickly diagnose patients and prevent them from coming into contact with healthy people. Computer methods are very effective in finding patients with COVID-19 and speed up the diagnosis. These methods are also widely used to assess a patient’s condition, for example, to assess the disease’s progression over time and to measure the rate of spread of the virus in the lungs. In this article, a segmentation method is introduced to segment the infected parts of the lung in CT scans. This method is based on Lazy-Snipping and Super-pixel algorithms. As a result of segmentation, the performance of algorithm is presented and compared with other methods using Dice score which was 80%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Determinants of Health and Depression among African American Adults: A Scoping Review of Current Research.

Depression in the United States (US) is increasing across all races and ethnicities and is attributed to multiple social determinants of health (SDOH). For members of historically marginalized races and ethnicities, depression is often underreported and undertreated, and can present as more severe. Limited research explores multiple SDOH and depression among African American adults in the US. Guided by Healthy People (HP) 2030, and using cross-disciplinary mental health terminology, we conducted a comprehensive search to capture studies specific to African American adults in the US published after 2016. We applied known scoping review methodology and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. From 12,315 initial results, 60 studies were included in our final sample. Most studies explored the HP 2030 Social and Community Context domain, with a heavy focus on discrimination and social support; no studies examined Health Care Access and Quality. Researchers typically utilized cross-sectional, secondary datasets; no qualitative studies were included. We recommend research that comprehensively examines mental health risk and protective factors over the life course within, not just between, populations to inform tailored health promotion and public policy interventions for improving SDOH and reducing racial and ethnic health disparities.

ICT Role during COVID-19 Pandemic in Lifelong Learning for Disabilities

Education of people with disabilities requires special attention because data from world organizations show that only 5% of people with disabilities receive a quality basic education. This study seeks to determine the possibility of improving the education of people with special needs through the use of ICT technologies during the covid-19 pandemic in continuing education, that is, lifelong learning. Even though most studies focus on the possibility of using ICT in the education of children with disabilities and are subject to the formation of an inclusive digital space, the feasibility of using basic knowledge of people with disabilities during their profile education and self-development with the help of ICT through distance or blended life-long learning becomes relevant. A review of the literature on the problem of research shows that when ICTs are transformed due to the use of additional technical or software tools, people with disabilities have the opportunity to life-long learning, obtain a profession, develop in it and move up the career ladder. At the same time, ICTs can be used to learn foreign languages and acquire a whole range of knowledge in a distance or blended format. © 2021 Universidad Tecnica de Manabi. All Rights Reserved.

The Conjunction of Deterministic and Probabilistic Events in Realistic Scenarios of Outdoor Infections

The aim of this paper is the derivation of an robust formalism that calculates the so-called social distancing as already determined in the ongoing Corona Virus Disease 2019 (Covid-19 in short) being established in various places in the world between 1.5 m and 2.5 m. This would constitutes a critic space of separation among people in the which aerosols might not be effective to infect healthy people. In addition to wearing masks and face protection, the social distancing appears to be critic to keep people far of infections and consequences produced from it. In this way, the paper has opted by the incorporation of a full deterministic model inside the equation of Weiss, by the which it fits well to the action of outdoor infection when wind manages the direction and displacement of aerosols in space. Thus, while a deterministic approach targets to propose a risk’s probability, a probabilistic scenario established by Weiss in conjunction to the deterministic events would yield an approximated model of outdoor infection when there is a continuous source of infected aerosols that are moving through air in according to a wind velocity. The simulations have shown that the present approach is valid to some extent in the sense that only the 1D case is considered. The model can be extended with the implementation of physical variables that can attenuate the presence of disturbs and random noise that minimizes the effectiveness of present proposal. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Healthy People and Interested Students: Medical and Pharmacy Students' Knowledge and Attitudes Regarding Public Health.

Little is known about health professions students' awareness and attitudes regarding public health in the United States. Therefore, the purpose of this study was to assess medical and pharmacy students' knowledge and interest in the Healthy People initiative as well as perceptions of public health content in their curricula. An electronic survey was distributed in March 2021 in seven schools across Ohio; participation was incentivized through a USD 5 donation to the Ohio Association of Foodbanks to aid in COVID-19 relief efforts (maximum USD 1000) for each completed survey. A total of 182 medical students and 233 pharmacy students participated (12% response rate). Less than one-third of respondents reported familiarity with Healthy People and correctly identified the latest edition. However, nearly all respondents agreed public health initiatives are valuable to the American healthcare system. Almost all students expressed a desire to practice interprofessionally to attain public health goals. Both medical and pharmacy students recognized core public health topics in their curricula, and nearly 90% wanted more information. These findings indicate that the majority of medical and pharmacy students in Ohio believe public health initiatives to be important, yet knowledge gaps exist regarding Healthy People. This information can guide curricular efforts and inform future studies of health professions students.