RESUMO
Background As the most basic unit of infectious disease prevention and control，community health service institutions are the frontline and important gateway for the prevention and control of infectious disease. Primary care physicians are responsible for epidemic surveillance，vaccination，health promotion and assistance to centers for disease control in investigating and disposing outbreaks and public health emergencies in their districts，and play an active role in disease prevention and control by groups，susceptible population protection，infectious source control and health education，as well as the effective prevention and control of infectious diseases. Objective To understand the ability of primary care physicians to diagnose and treat infectious diseases in the community，analyse their existing problems and shortcomings，design and conduct a series of intensive training related to infectious diseases for improving the capacity of infectious disease prevention and control at the primary level；To evaluate the effectiveness of online continuing medical education，so as to provide a reference for better continuing medical education on infectious diseases in the community. Methods All participants of the National Community Infectious Diseases Continuing Education Conference held by the Department of Family Medicine of the University of Hong Kong-Shenzhen Hospital in November 2021 were selected as research subjects from November 2021 to March 2022. The questionnaires were distributed to all registered attendees before and after the conference through the QR code of the questionnaire star，and the content of pre-conference questionnaire included demographic characteristics of the participants，participation in infectious disease training in the community since started working，diagnosis and treatment of infectious diseases in the community，subjective attitudes towards the prevention and control of infectious diseases in the community（willingness to manage infectious diseases in the community，satisfaction with their own infectious disease management skills），expertise in infectious disease prevention and control and knowledge related to conference content，attitude towards hepatitis B. The content of the post-conference questionnaire mainly included knowledge about the content of conference，attitude towards hepatitis B and satisfaction survey of this online conference. A total of 301 primary care physicians completed the questionnaire before and after the conference，and a total of 194 completed the questionnaire before and after the conference. Results Among all participants，166 （55.1%） had attended infectious disease training in the community，of whom 49（29.5%） were satisfied with their infectious disease diagnosis and treatment ability；135（44.8%） had not attended the training，of whom 22（16.3%） were satisfied with their infectious disease diagnosis and treatment ability. 143（86.1 %） of 166 participants who had attended infectious disease training in the community indicated their willingness to manage community infectious diseases，99（73.3%） of 135 participants who had not attended infectious disease training in the community indicated their willingness to manage community infectious diseases. 66（27.3%） of participants who were satisfied with their infectious disease diagnosis and treatment ability indicated their willingness to manage community infectious diseases. The top three infectious disease tests conducted by the institutions were hepatitis B，AIDS，and hepatitis C；the top three infectious diseases treated in the past six months were hepatitis B，influenza，hand，foot and mouth disease. Different self-evaluation and willingness to train may affect the willingness to manage community infectious diseases（P<0.05）. Among the participants who completed the questionnaire both before and after the conference，the highest correct answer rate for compulsory management of statutory infectious diseases before the conference was 89.7%，the owest accuracy rate for the type of disinfection of the COVID-19 infection was only 17.0%，the correct rates of other questions ranged from 34.0% to 40.7%. The correct rates of the questions after the conference were higher than those before the conference，and the correct rates ranged from 48.9% to 52.6%. The score of attitude towards hepatitis B after the conference was higher than that before the conference （P<0.05）. In terms of feedback after conference，254（98.1%） expressed satisfaction in the total of 259 questionnaires. In terms of suggestions for online conference，179（69.1%） and 174（67.2%） participants believed that online fluency and online interaction need to be improved. Conclusion The primary care physicians receive relatively less infectious diseases training in the community，inadequate infectious diseases training in the community can improve the confidence of self-competence，attitude of active management of infectious diseases and diagnosis and treatment ability in the primary care physicians. The future direction of continuing medical education should focus on the training of emerging infectious diseases and novel medical concepts，relevant experts should be invited to comment on the necessity and effectiveness of training in the community. © 2023 Chinese General Practice. All rights reserved.
RESUMO
Physical distancing is believed to contain the spread of COVID-19 virus. With data provided by COVID-19 Community Mobility Reports from Google, this study examines the mobility patterns at different stages of Movement Control Order (MCO) and investigates whether lower mobility reduces the number of new cases, death cases, and recovery rate. This research also covers the time spent in places such as i) retail and recreation, ii) grocery and pharmacy, iii) parks, iv) transit stations, v) the workplace, and vi) residential areas. As each of these types of places has distinct epidemiological characteristics, they may spread transmission differently. This study also correlates the stock market with mobility patterns of Malaysians in order to evaluate the effectiveness of lockdowns on COVID-19 incidents and its impact on the stock market, in this case;Kuala Lumpur Composite Index (KLCI). Findings of this study highlight the effects of pandemic COVID-19 on stock market daily performance by utilizing prospect and uncertainty theory in predicting the short-term impacts of the epidemic.
RESUMO
Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide. Traditional Chinese Medicine (TCM) was considered important by Chinese health authorities in the fight against COVID-19. This review systematically analyzed and evaluated the safety and efficacy of TCM combined with Western Medicine (WM) for the treatment of COVID-19. We sought to provide summary evidence for clinicians when using TCM. We searched for studies in PubMed, Web of Science, Embase, Medline, the Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data from database inception to June 1, 2021. Overall, 31 studies (14,579 participants) were involved in the final systematic review, including 15 randomized controlled trials and 16 observational studies. TCM combined with WM showed main outcomes of a higher clinical efficacy rate (odds ratio [OR] =2.48, 95% confidence interval [CI] =1.90-3.24, I 2 = 4%) and lower case fatality rate (OR = 0.31, 95% CI = 0.19-0.49, I 2 = 80%) compared with WM treatment alone. No significant overall adverse events were found between TCM plus WM group and WM group (OR = 1.43, 95% CI = 0.63-2.23, I 2 = 75%). Some larger randomized control trials would assist in defining the effect of TCM combined with WM on the treatment of COVID-19 complications such as cardiac injury. TCM combined with WM may be safe and effective for the treatment of COVID-19. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.
RESUMO
Environmental-friendly and efficient strategies for triclosan (TCS) removal have received more attention. Influenced by COVID-19, a large amount of TCS contaminants were accumulated in medical and domestic wastewater discharges. In this study, a unique g-C3N4/Bi2MoO6 heterostructure was fabricated and optimized by a novel and simple method for superb photocatalytic dechlorination of TCS into 2-phenoxyphenol (2-PP) under visible light irradiation. The as-prepared samples were characterized and analyzed by XRD, BET, SEM, XPS, etc. The rationally designed g-C3N4/Bi2MoO6 (4:6) catalyst exhibited notably photocatalytic activity in that more than 95.5% of TCS was transformed at 180 min, which was 3.6 times higher than that of pure g-C3N4 powder. This catalyst promotes efficient photocatalytic electron-hole separation for efficient dechlorination by photocatalytic reduction. The samples exhibited high recyclable ability and the dechlorination pathway was clear. The results of Density Functional Theory calculations displayed the TCS dechlorination selectivity has different mechanisms and hydrogen substitution may be more favorable than hydrogen ion in the TCS dechlorination hydrogen transfer process. This work will provide an experimental and theoretical basis for designing high-performance photocatalysts to construct the systems of efficient and safe visible photocatalytic reduction of aromatic chlorinated pollutants, such as TCS in dechlorinated waters. © 2022 Elsevier Ltd
RESUMO
Background:Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.Methods:In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.Results:The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.Conclusions:We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.
RESUMO
Food is the fundamental guarantee of people's lives, and the food industry has always occupied an essential position in the national economy. Profit growth rate, as a measure of an enterprise's development ability, can intuitively reflect the change in operating profit for food enterprises. The accurate prediction of profit growth rate can provide a decision-making reference for enterprises in planning business objectives in the next stage. However, many factors affect the profit variation of a company, and it is hard to make accurate predictions using traditional statistical economics forecasting methods. Since the Long-Short Term Memory (LSTM) model can capture nonlinear relationships in time series analysis, we propose an LSTM-based model to predict the profit growth rate of enterprises by using the operational data of four seasons ahead. Moreover, due to the COVID-19 pandemic, the impact of supply chain integrity on enterprise operations is increasing. We introduce the information of the supply chain owned by the enterprise to predict the profit growth rate of the enterprise. The result of our model exhibits high prediction accuracy, which indicates that our model could provide practical guidance for companies' production and operation activities. © 2022 The authors and IOS Press.
RESUMO
Influenced by the Covid-19 pandemic, fresh food e-commerce market in China developed quickly. Efficient solution for Order Batch Problem (OBP) could achieve efficient batching operation and then reduce costs and control risks. However, the OBP model proposed by the previous researches did not consider the characteristics of fresh food products such as the less demand of orders, the large variety of products, perishability of products and etc. Therefore, this paper proposed a model of OBP with freshness constraint of perishable food products, and proposed a two-stage heuristic algorithm to solve the target problem of the model. Our solution could improve the efficiency of the sorting process while ensuring the freshness of food products. © 2022 The authors and IOS Press.
RESUMO
During the Covid-19 global pandemic, exposure to cold cargo surfaces contaminated with Covid-19 was first identified as a potential cause of infection. Given that the epidemic situation in China is basically stable, epidemic prevention and control of cold chain cargo handling operations in Chinese ports is one of the key points for the country. It is concluded that the main risk link that may cause the spread of the epidemic is the unpacking of cold chain cargo containers by analyzing the process and characteristics of port cold chain cargo handling.In order to prevent imported epidemics from abroad, Chinese ports have taken countermeasures such as virus detection and sterilization. At present, nucleic acid detection measures have been adopted for the virus detection on the surface of goods, but the sampling quantity and method are lack of unified regulations, and the detection takes a long time. The mobile cabin PCR laboratories are used in some areas to improve the timeliness, and the virus detection on the surface of goods needs more sensitive and rapid detection technology. In the process of comprehensive preventive disinfection of goods, it was found that the disinfection efficiency of common disinfectants in low-temperature environment was greatly reduced, and a variety of new low-temperature disinfectants were rapidly developed. The disinfection technology based on deep UV LED, UV catalysis, nuclear radiation and other physical technologies have brought a new revolution to the disinfection of the new coronavirus on the surface of low-temperature objects.Due to the global pandemic of novel coronavirus and its continuous variation, technical measures for epidemic prevention and control have developed rapidly. From the prevention and control experience of Chinese ports in combating the epidemic, epidemic prevention and control is a systematic project, which requires the combination of various technical measures and close cooperation of multiple links. © 2022 SPIE.
RESUMO
Masked face recognition has made great progress in the field of computer vision since the popularity of COVID-19 epidemic in 2020. In countries with severe outbreaks, people are required to wear masks in public. The current face recognition methods, which take use of the whole face as input data, are quite well established. However, while people are use of face masks, it will reduce the accuracy of face recognition. Therefore, we propose a mask wearing recognition method based on MobileNetV2 and solve the problem that many of models cannot be applied to portable devices or mobile terminals. The results indicate that this method has 98.30% accuracy in identifying the masked face. Simultaneously, a higher accuracy is obtained compared to VGG16. This approach has proven to be working well for the practical needs. © 2022 ACM.
RESUMO
The Covid-19 pandemic has altered the way people view online learning, which has experienced explosive growth since the pandemic and quickly cultivated the habit of online learning among the students. Online learning is not limited by time or space, and students can learn the courses repeatedly. Despite these advantages, it still has serious limitations in face of hardware-related courses like embedded technology, because it is not likely to provide opportunities for close-up observation of the real objects or any hands-on experience. Therefore, the learning effect will be greatly compromised. This paper proposed a solution for online learning of embedded technology course. It introduced the objective and content design of the course, the integration of ideological and political elements into the course, the teaching platforms that have been used as well as the concrete implementation process of online learning of the course, and it also statistically examined the effect of online learning. © 2022 ACM.
RESUMO
Objective: To explore the relationship of health literacy with COVID-19 prevention and control knowledge, attitude and practice (KAP) in general population so as to contribute scientific evidence for strengthening health education and promoting health literacy to resist the threat of major infectious disease outbreaks. Methods: In September 2020, a questionnaire survey was conducted in residents selected by a multi-stage random sampling across all the twelve counties/districts of Baoji city. The questionnaire, which was issued by the Chinese Health Education Center, consisted of a health literacy questionnaire and a COVID-19 prevention and control KAP questionnaire. According to the national unified scoring method, the participants were divided into two groups: those who met and those who failed to meet the overall standard of health literacy. The results of the answer to each KAP question were compared between the two groups by Chi-square test or rank sum test. Multivariate binary Logistic regression was used to control confounding effects of socio-demographic characteristics to draw relatively reliable conclusions. Results: A total of 4 544 valid questionnaires were collected, in which 664(14.60%)met the overall standard of health literacy, but 3 880(85. 40%)failed to do so. Compared with the unmet group, the met group had a higher correct answer rate in 10 of the 11 knowledge-related questions(all P<0.001);showed more positive answer to each attitude-related question in the three aspects, namely, responsibility for the prevention and control of infectious disease transmission, evaluation for COVID-19-related information release and reporting, and evaluation for the government's COVID-19 prevention and control results (all P<0.001);and acted more actively in 6 of the 7 practice concerning appropriate self-prevention and control behaviors during the COVID-19 outbreak(all P<0.001). Logistic regression analyses confirmed that achieving the overall standard of health literacy played a positive role in each of the contents of COVID-19 prevention and control KAP in study(ORs were between 1.44 and 4.09, all P<0.001). Moreover, the absolute value of regression coefficient of the overall standard of health literacy was the largest compared with all the socio-demographic factors. Logistic regression was used to further analyze relationships between each of the six health dimensions of health literacy and COVID -19 prevention and control KAP, which revealed that the association with safety and first aid, infectious diseases prevention, and health information was the closest. Conclusion: Health literacy is closely related to COVID-19 prevention and control KAP in the general population of Baoji city. Promoting residents' health literacy by targeted health education can play an important and positive role in dealing with the threat of major infectious diseases outbreaks.
RESUMO
A complex pattern of urban demographic transition has been taking shape since the onset of the COVID-19 pandemic. The long-standing rural-to-urban route of population migration that has propelled waves of massive urbanization over the decades is increasingly being juxtaposed with a reverse movement, as the pandemic drives urban dwellers to suburban communities. The changing dynamics of the flow of residents to and from urban areas underscore the necessity of comprehensive urban land-use mapping for urban planning/management/assessment. These maps are essential for anticipating the rapidly evolving demands of the urban populace and mitigating the environmental and social consequences of uncontrolled urban expansion. The integration of light detection and ranging (LiDAR) and imagery data provides an opportunity for urban planning projects to take advantage of its complementary geometric and radiometric characteristics, respectively, with a potential increase in urban mapping accuracies. We enhance the color-based segmentation algorithm for object-based classification of multispectral LiDAR point clouds fused with very high-resolution imagery data acquired for a residential urban study area. We propose a multilevel classification using multilayer perceptron neural networks through vectors of geometric and spectral features structured in different classification scenarios. After an investigation of all classification scenarios, the proposed method achieves an overall mapping accuracy exceeding 98%, combining the original and calculated feature vectors and their output space projected by principal components analysis. This combination also eliminates some misclassifications among classes. We used splits of training, validation, and testing subsets and the k-fold cross-validation to quantitatively assess the classification scenarios. The proposed work improves the color-based segmentation algorithm to fit object-based classification applications and examines multiple classification scenarios. The presented scenarios prove superiority in developing urban mapping accuracies. The various feature spaces suggest the best urban mapping applications based on the available characteristics of the obtained data. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
RESUMO
During the COVID-19 pandemic, countries applied trade restrictions to insulate their domestic markets from the world market. However, these trade policies could have amplified international market price fluctuations. This paper explores the effects of trade restrictions on international agricultural price volatility. A theoretical model is developed to quantify how trade policies amplify the initial shock. Using panel data covering 71 countries from January 2020 to July 2021, we examine empirically the effects of trade policies on world agricultural price volatility. The results show that trade distortions further induced volatility of world agricultural prices by around 22 percent during the COVID-19 pandemic. The multiplier effects are much more substantial in agricultural exporting countries than in importing countries. Large countries like China and the US could make significant contributions to stabilizing world prices by limiting the extent of unilateral trade policy interventions.
RESUMO
With epidemics and pandemics like COVID-19, many offline healthcare services have been suspended and shifted to online, where patients and doctors typically communicate by typing texts. The limited communication poses a threat to the service quality of E-health, and also raises higher demand on the language skills of doctors, in which medical terms are a common concern. Traditional studies of offline healthcare mostly hold a negative attitude towards the use of medical terms by doctors. However, should we still advise doctors to avoid using medical terms in E-health? To answer this question, this paper conducts a study combining technical and empirical analyses based on real data. In this paper, a novel unsupervised text-mining method is proposed to automatically identify medical terms with crowd wisdom from large-scale doctor-patient communication texts. Then, a TREC-type experiment is carried out to validate the proposed method in terms of Precision, Recall, and F1-measure, demonstrating that it can identify accurate and comprehensive medical terms. Based on the identified medical terms, an empirical analysis is conducted to verify the influence of medical terms used by doctors on the service quality of E-health. The analysis results show that for patients with low health literacy, the use of medical terms by doctors would decrease their service quality. However, for patients with high health literacy, the use of medical terms by doctors can significantly increase their service quality, revealing that doctors could improve their service quality in E-health by adjusting their medical term usage according to the health literacy of patients. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
RESUMO
Increasing cases of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presenting with severe Kawasaki-like disease have recently been reported in some Western countries, raising the possibility of SARS-CoV-2 being a trigger of Kawasaki disease (KD). We aimed to investigate whether KD is linked to coronavirus disease 2019 (COVID-19) in Chinese pediatric population. Methods: Patients were enrolled if diagnosed with KD in the 40 hospitals of China Kawasaki Disease Research Collaborative Group from January to April 2020, the COVID-19 epidemic period in China. Information of demographic data, KD shock syndrome, macrophage activation syndrome (MAS), evidence of SARS-CoV-2 infection and the number of KD cases were retrospectively analyzed. Results: The completed response was received from 29/40 hospitals (72.5%) across 19 provinces. Of 2,108 KD patients enrolled, the median age was 1.9 years and 63.8% were male. KD shock syndrome and MAS were diagnosed in 8 (0.4%) and 2 (0.1%) patients, respectively, none of whom had contact history with COVID-19 patients. A greater number of KD cases from January to April 2020 than the upper limit of 95% confidence interval (95% CI) of estimated numbers of cases of the past 3 years were observed in only 2 out of 29 (6.9%) hospitals. Reverse transcription-polymerase chain reaction (RT-PCR) tests in 434 patients and antibody tests in 64 patients for SARS-CoV-2 were all negative, including nine with exposure history. Conclusions: There is no evidence of the link of KD with COVID-19 in Chinese children in terms of its prevalence and severity. © Pediatric Medicine. All rights reserved.
RESUMO
The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.
RESUMO
The COVID-19 outbreak has a significant impact on the health and well-being of the global population. The first step in the fight against COVID-19 is effective screening of infected patients. A key screening method is chest radiography. In early studies, patients showed abnormalities on chest radiographs, which were characteristic of patients with COVID-19 infection. In this paper, we use the open dataset covidx to train the neural network. covidx is composed of 13,800 chest radiographs with 13725 patients from three open case access data repositories. Novel coronavirus pneumonia was used to identify novel coronavirus pneumonia, common pneumonia and new crown pneumonia images. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
RESUMO
New Year's Eve 2020, Wuhan outbreak of new crown pneumonia and spread nationwide. This global epidemic has brought great challenges to the operation of emergency logistics system in China In the face of this major public health emergency test, we can note that there are still many shortcomings in the current supply chain system of emergency logistics in China This paper summarizes the experience and lessons accumulated in the anti-epidemic process, aims at all kinds of short boards of the current emergency logistics system and gives the idea of constructing and perfecting it. © The Authors, published by EDP Sciences, 2021.