Fabrication of a High-Performance and Reusable Planar Face Mask in Response to the COVID-19 Pandemic

The coronavirus disease 2019(COVID-19)pandemic has caused a surge in demand for face masks,with the massive consumption of masks leading to an increase in resou...     

基于公共卫生设施应急背景下新型分类垃圾桶设计思考--以新冠肺炎疫情为例

近年来,公共卫生突发事件层出不穷,对公众健康和社会安全造成了极大的危害。为了减少公共卫生突发事件带来的危害,加强公共卫生设施建设和公共卫生防护工作,文章以2020年新冠肺炎疫情为例,对我国垃圾分类现状和现有分类垃圾桶存在的问题进行了调查分析,整理归纳了普通生活垃圾桶和"防疫废弃物"专用桶的设计要点,最终对新型分类垃圾桶在功能、造型、结构、标识上提出了新的设计构思。     

Sustainable Supplier Selection Model in Supply Chains During the COVID-19 Pandemic

As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries. Therefore, it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process. While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems, few of these models consider the problem under the condition of a fuzzy decision-making environment. The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process (FAHP) model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to assist the supplier selection process in the garment industry in a fuzzy decision-making environment. In this study, the FAHP method is used to evaluate the performance and the weight of each criterion. TOPSIS is then used to rank all potential suppliers. The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability. The results from the case study provide empirical evidence that the model is feasible. The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.     

Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settings effectively manage COVID-19.     

Buying Time for an Effective Epidemic Response: The Impact of a Public Holiday for Outbreak Control on COVID-19 Epidemic Spread

Rapid responses in the early stage of a new epidemic are crucial in outbreak control. Public holidays for outbreak control could provide a critical time window for a rapid rollout of social distancing and other control measures at a large population scale. The objective of our study was to explore the impact of the timing and duration of outbreak-control holidays on the coronavirus disease 2019 (COVID-19) epidemic spread during the early stage in China. We developed a compartment model to simulate the dynamic transmission of COVID-19 in China starting from January 2020. We projected and compared epidemic trajectories with and without an outbreak-control holiday that started during the Chinese Lunar New Year. We considered multiple scenarios of the outbreak-control holiday with different durations and starting times, and under different assumptions about viral transmission rates. We estimated the delays in days to reach certain thresholds of infections under different scenarios. Our results show that the outbreak-control holiday in China likely stalled the spread of COVID-19 for several days. The base case outbreak-control holiday (21 d for Hubei Province and 10 d for all other provinces) delayed the time to reach 100 000 confirmed infections by 7.54 d. A longer outbreak-control holiday would have had stronger effects. A nationwide outbreak-control holiday of 21 d would have delayed the time to 100 000 confirmed infections by nearly 10 d. Furthermore, we find that outbreak-control holidays that start earlier in the course of a new epidemic are more effective in stalling epidemic spread than later holidays and that additional control measures during the holidays can boost the holiday effect. In conclusion, an outbreak-control holiday can likely effectively delay the transmission of epidemics that spread through social contacts. The temporary delay in the epidemic trajectory buys time, which scientists can use to discover transmission routes and identify effective public health interventions and which governments can use to build physical infrastructure, organize medical supplies, and deploy human resources for long-term epidemic mitigation and control efforts.     

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.     

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79–116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.     

E-Learning during COVID-19 Outbreak: Cloud Computing Adoption in Indian Public Universities

In the COVID-19 pandemic situation, the need to adopt cloud computing (CC) applications by education institutions, in general, and higher education (HE) institutions, in particular, has especially increased to engage students in an online mode and remotely carrying out research. The adoption of CC across various sectors, including HE, has been picking momentum in the developing countries in the last few years. In the Indian context, the CC adaptation in the HE sector (HES) remains a less thoroughly explored sector, and no comprehensive study is reported in the literature. Therefore, the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption (CCA) by HE institutions (HEIs) in India. The scope of the study is limited to public universities (PUs) in India. There are, in total, 465 Indian PUs and among these 304 PUs, (i.e., 65% PUs) are surveyed using questionnaire-based research. The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model (TAM), Technology-Organization-Environment (TOE), and Diffusion of Innovation (DOI) in the context of the HES. This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling (SEM) and confirmatory factor analysis (CFA). The findings reveal that competitive advantage (CA), technology compatibility (TC), technology readiness (TR), senior leadership support, security concerns, government support, and vendor support are the significant contributing factors of CCA by Indian PUs. The study contends that whereas the rest of the factors positively affect the PUs’ intention towards CCA, security concerns are a significant reason for the reluctance of these universities against adopting CC. The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs. Further, the study has given useful insights into the successful CCA by Indian PUs, which will facilitate eLearning and remote working during COVID-19 or similar outbreak.     

突发公共卫生事件下医疗防护用品供应链协调

针对一个生产商-第三方物流提供商-零售商组成的医疗防护用品三级供应链,在考虑随机性需求的基础上,利用收益共享契约来探讨突发事件对三级供应链的影响。研究表明：突发事件下,供应链成员企业通过调整收益共享契约中转移支付的产品批发价格及物流服务价格参数,可使收益共享契约具有抗突发事件性,同时也使得供应链收益在生产商、物流商和零售商之间重新分配,实现了供应链企业共赢。     

Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia

With increasing frequency, people are using social media sites to obtain timely information about the world's grand challenges and this phenomenon is amplified during crises. However, little research has been conducted to determine how people participate and how their involvement can be promoted on social media sites, although the critical role played by those sites has been well documented. Based on the theory of planned behavior (TPB), this study develops and tests a theoretical model to establish the effect of several factors with survey data collected during the COVID-19 pandemic, in Saudi Arabia. The relationship was verified on a sample of 213 respondents active on Twitter, using Partial Least Square (PLS). The study found that attitude, perceived behavioural control and subjective norm affect Twitter users' active participation significantly within the context of a time of crisis. It also found a positive effect of utilitarian and hedonic values and trust. These results will provide a more comprehensive evaluation of Twitter users in grand challenges (and more specifically during a crisis) and furnish academics and managers with instructive guidance.     

Biotechnology: Present and Future Roles in the Pharmaceutical Industry

Abstract

Biotechnology is the integration of a number of scientific disciplines including microbiology, genetics, biochemistry and chemical engineering. It uses living organisms, or systems or products from these organisms to make or modify useful products. New biotechnology comprises genetic engineering, protoplast fusion and monoclonal antibody techniques, powerful new “tools” designed to generate efficient bioprocesses and products for the pharmaceutical industry. The following areas of biotechnology are highlighted: human insulin, interferons and other growth factors, neuroactive peptides, blood products, antibiotics, enzymes, monoclonal antibodies, vaccines and oncogenes.     

Biotechnology: Present and Future Roles in the Pharmaceutical Industry

Biotechnology is the integration of a number of scientific disciplines including microbiology, genetics, biochemistry and chemical engineering. It uses living organisms, or systems or products from these organisms to make or modify useful products. New biotechnology comprises genetic engineering, protoplast fusion and monoclonal antibody techniques, powerful new “tools” designed to generate efficient bioprocesses and products for the pharmaceutical industry. The following areas of biotechnology are highlighted: human insulin, interferons and other growth factors, neuroactive peptides, blood products, antibiotics, enzymes, monoclonal antibodies, vaccines and oncogenes.     

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom of Saudi Arabia (KSA) is additionally combating with the COVID-19 danger as the cases announced till June 15, 2020 reached the count of 132,048 with 1,011 deaths. According to the report released by the KSA on June 14, 2020, more than 4,000 cases of COVID-19 pandemic had been registered in the country. Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease, our examination expects to assess the severity of COVID-19 pandemic in cities of KSA. In addition, computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease. Furthermore, a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia. Further, this paper has taken the Fuzzy Analytic Hierarchy Process (AHP) technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA. The discoveries and the proposed structure would be a practical, expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA. Hence these urban zones clearly emerge as the COVID-19 hotspots. The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic. The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.     

COVID-19 Vaccine Allocation: Modeling Health Outcomes and Equity Implications of Alternative Strategies

Given the scarcity of safe and effective COVID-19 vaccines, a chief policy question is how to allocate them among different sociodemographic groups. This paper evaluates COVID-19 vaccine prioritization strategies proposed to date, focusing on their stated goals; the mechanisms through which the selected allocations affect the course and burden of the pandemic; and the main epidemiological, economic, logistical, and political issues that arise when setting the prioritization strategy. The paper uses a simple, age-stratified susceptible–exposed–infectious–recovered model applied to the United States to quantitatively assess the performance of alternative prioritization strategies with respect to avoided deaths, avoided infections, and life-years gained. We demonstrate that prioritizing essential workers is a viable strategy for reducing the number of cases and years of life lost, while the largest reduction in deaths is achieved by prioritizing older adults in most scenarios, even if the vaccine is effective at blocking viral transmission. Uncertainty regarding this property and potential delays in dose delivery reinforce the call for prioritizing older adults. Additionally, we investigate the strength of the equity motive that would support an allocation strategy attaching absolute priority to essential workers for a vaccine that reduces infection-fatality risk.     

健康风险沟通中信息图形设计的易读性与可及性研究——以新冠疫情为例

目的 在持续的公共卫生危机期间,以信息图形提升公众对健康风险的认识是必要的手段之一。但信息图形涉及对数据、概念、流程等科学话语的视觉转译,公众可能会面临信息理解的易读性和可及性的问题。确认何种健康信息及其图形感知的设计可提升对风险概念的有效理解。方法 以新冠信息图形为例,基于风险传播、风险大小、风险接触和风险规避4个典型的风险沟通的类型,展开了一项联合社区用户、专家和设计师的焦点小组研究。结果 研究得到了5项设计建议,包括简化信息但不牺牲信息的丰富性,提供风险背景的上下文,数值比例的图形转换,感知-概念的图文整合,区分受众不同健康素养水平,谨慎处理信息的伦理问题。结论 健康风险信息紧急而复杂。信息设计可以提前介入,理解细分人群的信息行为、信息环境,快速响应沟通策略。研究得出的相关结论可对健康风险沟通实践提供参考,亦可拓展信息设计对健康传播跨学科研究的新范式。     

Managing the risk of a COVID-19 outbreak from border arrivals

In an attempt to maintain the elimination of COVID-19 in New Zealand, all international arrivals are required to spend 14 days in government-managed quarantine and to return a negative test result before being released. We model the testing, isolation and transmission of COVID-19 within quarantine facilities to estimate the risk of community outbreaks being seeded at the border. We use a simple branching process model for COVID-19 transmission that includes a time-dependent probability of a false-negative test result. We show that the combination of 14-day quarantine with two tests is highly effective in preventing an infectious case entering the community, provided there is no transmission within quarantine facilities. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increases the risk of an infectious case being released. We calculate the fraction of cases detected in the second week of their two-week stay and show that this may be a useful indicator of the likelihood of transmission occurring within quarantine facilities. Frontline staff working at the border risk exposure to infected individuals and this has the potential to lead to a community outbreak. We use the model to test surveillance strategies and evaluate the likely size of the outbreak at the time it is first detected. We conclude with some recommendations for managing the risk of potential future outbreaks originating from the border.     

The history of science and medicine in the context of COVID-19

This spotlight issue encourages reflection on the current COVID-19 pandemic, not simply through comparisons with previous epidemics, but also by illustrating that epidemics deserve study within their broader cultural, political, scientific, and geographic contexts. Epidemics are not solely a function of pathogens; they are also a function of how society is structured, how political power is wielded in the name of public health, how quantitative data is collected, how diseases are categorised and modelled, and how histories of disease are narrated. Each of these activities has its own history. As historians of science and medicine have long pointed out, even the most basic methodologies that underpin scientific research—observation, trust in numbers, the use of models, even the experimental method itself—have a history. They should not be taken as a given, but understood as processes, or even strategies, that were negotiated, argued for and against, and developed within particular historical contexts and explanatory schemes. Knowing the history of something—whether of numbers, narratives, or disease—enables us to see a broader range of trajectories available to us. These varied histories also remind us that we are currently in the midst of a chaotic drama of uncertainty, within our own unstable and unfolding narrative.