ABSTRACT
Ninety-six million people are symptomatically infected with Dengue globally every year. Under the current standard-of-care, up to 20% of Dengue patients may be hospitalized, while only 500,000 develop Dengue Haemorrhagic Fever (DHF) and require hospitalization. This leads to unnecessary overwhelming of hospitals in tropical countries during large Dengue epidemics, especially when healthcare systems are grappling with large numbers of COVID-19 patients. Our research team set out to discover biomarkers to prognosticate Dengue patients, and augment the infectious disease clinician's decision-making process to hospitalize Dengue patients. Host biomarkers with concentrations significantly different between pooled serum samples of Dengue Fever (DF) patients and DHF patients were identified using protein array. The prognostication capabilities of selected biomarkers were then validated over 283 adult Dengue patients recruited from three Singapore tertiary hospitals, prior to the diagnosis of DHF. Three biomarkers (A2M, CMA1 and VEGFA) were identified that provide independent prognostication value from one another, and from clinical parameters commonly monitored in Dengue patients. The combination of all three biomarkers was able to identify from as early as Day 1 after the onset of fever, DF patients whose conditions will deteriorate into DHF. The biomarkers are robust and able to predict DHF well when trained on different AI/ML algorithms (logistic regression, support vector machine, decision tree, random forest, AdaBoost and gradient boosting). When stacked, prediction models based on the biomarkers were able to predict DHF with 97.3% sensitivity, 92.7% specificity, 66.7% PPV, 99.6% NPV and an AUC of 0.978. To the best of our knowledge, our panel of three biomarkers offers the highest accuracy in prognosticating Dengue to date. Further studies are required to validate the biomarkers in different geographical settings and pilot their implementation as part of the standard-of-care workflow for Dengue patients. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. 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 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 . (Copyright applies to all s.)
ABSTRACT
This study analyzed the effect of anti-Asian American violence on Asian Americans' daily lives because the hate crimes or sentiment was not salient in the early stage of the pandemic in rural Alabama. The survey was conducted from April to May 2020. A total of 234 Laotians and 119 Cambodians participated, and multiple regression models were employed. Two communities demonstrated distinctive sociodemographic characteristics. The younger Cambodians were more concerned about anti-Asian violence, which made sense considering that Cambodians gained new community members through international marriage brides from Cambodia. They were more likely to obtain limited information due to the language barrier and depend on advice from leaders they could trust. These results explained the higher worry about the infection for younger Cambodians, the significant influence of community leaders' recommendations, and the higher fear by the educated. Laotians showed an overall moderating effect of age. Laotian fifties demonstrated that older adults handled better on the perceived disruption of COVID-19. They utilized various media sources to reduce their worry and help more appropriate damage-avoiding behavior for community members. © 2023 Transnational Press London Ltd. All rights reserved.
ABSTRACT
The COVID-19 pandemic has created an unprecedented health and financial crisis across the world. This crisis has not shown any sign of abatement despite world-wide efforts to contain the spread of the virus with draconian measures such as extensive social distancing, travel restrictions, and lockdowns. An inherent difficulty in controlling this crisis has disrupted the entire global supply chain and consequently threatened our livelihood and freedom. Faced with this daunting crisis, many businesses regardless of their sizes desperately need a viable survival strategy. To fill such a need, this paper reinvents crisis management tools and develops a strategy map for mitigating the adverse impact of COVID-19 on global supply chain operations, while enhancing supply chain resilience in times of turbulence and uncertainty. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
Assessing the impact of the COVID-19 crisis on economies is fundamental to tailor the responses of the governments to recover from the crisis. In this paper, we present a novel approach to assessing the economic impact with a large-scale credit card transaction dataset at a fine granularity. For this purpose, we develop a fine-grained economic-epidemiological modeling framework COVID-EENet, which is featured with a two-level deep neural network. In support of the fine-grained EEM, COVID-EENet learns the impact of nearby mass infection cases on the changes of local economies in each district. Through the experiments using the nationwide dataset, given a set of active mass infection cases, COVID-EENet is shown to precisely predict the sales changes in two or four weeks for each district and business category. Therefore, policymakers can be informed of the predictive impact to put in the most effective mitigation measures. Overall, we believe that our work opens a new perspective of using financial data to recover from the economic crisis. For public use in this urgent problem, we release the source code at https://github.com/kaist-dmlab/COVID-EENet.
ABSTRACT
Since the outbreak of coronavirus disease 2019 (COVID-19) has resulted in a dramatic loss of human life and economic disruption worldwide from early 2020, numerous studies focusing on COVID-19 forecasting were presented to yield accurate predicting results. However, most existing methods could not provide satisfying forecasting performance due to tons of assumptions, poor capability to learn appropriate parameters, etc. Therefore, in this paper, we combine a traditional time series decomposition: local mean decomposition (LMD) with temporal convolutional network (TCN) as a general framework to overcome these shortcomings. Based on the particular architecture, it can solve weekly new confirmed cases forecasting problem perfectly. Extensive experiments show that the proposed model significantly outperforms lots of state-of-the-art forecasting methods, and achieves desirable performance in terms of root mean squared log error (RMSLE), mean absolute percentage error (MAPE), Pearson correlation (PCORR), and coefficient of determination (R-2). To be specific, it could reach 0.9739, 0.8908, and 0.7461 on R-2 when horizon is 1, 2, and 3 respectively, which proves the effectiveness and robustness of our LMD-TCN model.
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Purpose: This paper aims to examine how significantly the COVID-19 pandemic affects supply chain operations and how the firms have reacted to the COVID-19-induced supply chain crisis. In addition, this paper investigates whether the micro, small and medium enterprises (MSMEs) are affected disproportionally by the COVID-19-induced supply chain crisis. Design/methodology/approach: This paper developed a series of hypotheses and tested them using cross-tabulation, canonical correlation, discriminant and exploratory factor analyses of the empirical data. Findings: The descriptive data analysis and hypothesis test results revealed that the labor productivity of the manufacturing, logistics and healthcare industry sectors was affected disproportionally by the COVID-19-induced crisis. However, outsourcing and global sourcing practices themselves did not necessarily exacerbate the severity of supply chain disruptions caused by COVID-19. The authors also found that MSMEs were adversely affected by the COVID-19 pandemic to a different degree than their large counterparts. Originality/value: This paper is one of the first of its kind to assess the extent of the COVID-19 adverse impact on supply chain operations using the exploratory analysis of the data collected from the questionnaire survey of US firms representing various industries sectors. © 2022, Emerald Publishing Limited.
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We present a sniffing bead system used to diagnose olfactory dysfunction in coronavirus disease 2019 (COVID-19) patients. A 25-year-old male presented with the loss of olfaction one week after he was diagnosed with severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). He had no other symptoms such as fever or myalgia but only showed an absence of respiratory distress. Nasal endoscopy and paranasal sinus CT showed that the patient had no bilateral sinus diseases;cranial nerve MRI showed no abnormal signal intensity or enhancement. A sniffing bead system was applied using 2-phenylethyl alcohol for the objective assessment of olfactory dysfunction to confirm the presence of anosmia. Anosmia was diagnosed early by objective evaluation using a sniffing bead system and early intervention with olfactory training. This case report suggests that a verified, one-off system for objective measurement of olfactory dysfunction in COVID-19 with olfactory training in patients could facilitate the recovery of olfactory function. Copyright © 2022 Korean Society of Otorhinolaryngology-Head and Neck Surgery
ABSTRACT
Throughout the COVID-19 pandemic, the most common symptom displayed by patients has been a fever, leading to the use of temperature scanning as a preemptive measure to detect potential carriers of the virus. Human employees with handheld thermometers have been used to fulfill this task, however this puts them at risk as they cannot be physically distanced and the sequential nature of this method leads to great inconveniences and inefficiency. The proposed solution is an autonomously navigating robot capable of conversing and scanning people's temperature to detect fevers and help screen for COVID-19. To satisfy this objective, the robot must be able to (1) navigate autonomously, (2) detect and track people, and (3) get individuals' temperature reading and converse with them if it exceeds 38 degrees C. An autonomously navigating mobile robot is used with a manipulator controlled using a face tracking algorithm, and an end effector consisting of a thermal camera, smartphone, and chatbot. The goal of this project is to develop a functioning solution that performs the above tasks. In addition, technical challenges encountered, and their engineering solutions will be presented, and recommendations will be made for enhancements that could be incorporated when approaching commercialization.
ABSTRACT
A novel coronavirus (SARS-CoV-2) causing corona virus disease 2019 (COVID-19) has attracted global attention due to its highly infectious and pathogenic properties. Most of current studies focus on aerosols released from infected individuals, but the presence of SARS-CoV-2 in wastewater also should be examined. In this review, we used bibliometrics to statistically evaluate the importance of water-related issues in the context of COVID-19. The results show that the levels and transmission possibilities of SARS-CoV-2 in wastewater are the main concerns, followed by potential secondary pollution by the intensive use of disinfectants, sludge disposal, and the personal safety of workers. The presence of SARS-CoV-2 in wastewater requires more attention during the COVID-19 pandemic. Thus, the most effective techniques, i.e., wastewater-based epidemiology and quantitative microbial risk assessment, for virus surveillance in wastewater are systematically analyzed. We further explicitly review and analyze the successful operation of a sewage treatment plant in Huoshenshan Hospital in China as an example and reference for other sewage treatment systems to properly ensure discharge safety and tackle the COVID-19 pandemic. This review offers deeper insight into the prevention and control of SARS-CoV-2 and similar viruses in the post-COVID-19 era from a wastewater perspective.
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Purpose: To identify sources of the success and failure of COVID-19 control measures and develop best-practice public health policy in mitigating the spread of COVID-19, this paper aims to evaluate the efficiency of various combinations of government COVID-19 control measures among OECD countries. This paper also identifies which factors critically influence the efficiency of COVID-19 control measures. Design/methodology/approach: This paper employed two-stage network SBM (slacks-based measure of efficiency) models with variable returns-to-scale and constant returns-to-scale, respectively, among various forms of data envelopment analysis (DEA) models. As a post hoc analysis, the authors used Tobit regression for examining the causal relationship between a nation's cultural dimensions and its COVID-19 control measure's efficiency scores. Findings: The authors found that the pervasive less individualistic and higher uncertainty avoiding culture positively influenced the efficient control of COVID-19 outbreaks since such a culture helped the government impose its mandatory COVID-19 control measures without people's strong resistance to those measures. Originality/value: Many public health policymakers are wondering why COVID-19 control measures are not effective in coping with the COVID-19 outbreaks. This paper helps the government find the most efficient combination of COVID-19 controls measures for curbing the spread of the stubborn coronavirus. This paper is one of the first attempts to identify pandemic risk mitigation factors from a cultural perspective. © 2021, Emerald Publishing Limited.
ABSTRACT
Coronavirus has affected the entire global community owing to its transmission through respiratory droplets. This has led to the mandatory usage of surgical masks for protection against this lethal virus in many countries. However, the currently available disposable surgical masks have limitations in terms of their hydrophobicity and reusability. Here, we report a single-step spray-coating technique for the formation of a superhydrophobic layer of single-walled carbon nanotubes (SWCNTs) on a melt-blown polypropylene (PP) surgical mask. The sprayed SWCNTs form a nanospike-like architecture on the PP surface, increasing the static contact angle for water from 113.6° ± 3.0° to 156.2° ± 1.8° and showing superhydrophobicity for various body fluids such as urine, tears, blood, sweat, and saliva. The CNT-coated surgical masks also display an outstanding photothermal response with an increase in their surface temperature to more than 90 °C within 30 s of 1 sun solar illumination, confirming its self-sterilization ability. Owing to the cumulative effect of the superhydrophobicity and photothermal performance of the SWCNTs, the CNT-coated masks show 99.99% higher bactericidal performance toward Escherichia coli than pristine masks. Further, the virucidal ability of the SWCNT-coated mask, tested by using virus-like particles, was found to be almost 99% under solar illumination. As the spray-coating method is easily scalable, the nanotube-coated mask provides cost-effective personal protection against respiratory diseases. © 2021 American Chemical Society.
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Background & Aim: Mesenchymal stromal cells (MSCs) are an investigational cell therapy for inflammatory diseases. Although they have robust anti-inflammatory properties, their success has been variable in clinical trials due to an unclear understanding of their mechanism. Once injected, a majority of MSCs traffic to the lung, where they are rapidly cleared, signifying an opportunity to target lung inflammatory [Figure presented] conditions such as acute respiratory distress syndrome (ARDS). ARDS is a catastrophic condition of the lungs, involving pulmonary inflammation that develops with severe SARS-CoV2 and other respiratory infections. MSCs are expected to prevent alveolar damage by suppressing the immune response and there is evidence that MSCs protect in phase I/IIa trials for ARDS associated with COVID-19. While these results are promising, understanding the mechanism is critical to determine dosing, maximize efficacy, and ultimately lead to an approved product. Methods, Results & Conclusion: We and others have demonstrated that within their short time in the lung, MSCs interact with monocytes and macrophages. Through direct cell contact, MSCs transfer cytoplasmic components, notably cytoplasmic processing-bodies (p-bodies) to monocytes and macrophages. P-bodies are membrane-less organelles that contain RNA binding proteins, microRNAs, and mRNAs enriched for genes that regulate the transcriptional landscape of cells. MSC interactions result in long-term transcriptional reprogramming of monocytes and macrophages to suppress a helper T cell response and upregulate tissue repair pathways. To investigate the mechanisms of MSCs in vivo, we utilized 2 mouse models of lung inflammation: 1. intranasal lipopolysaccharide (LPS) to study general acute inflammation and 2. an engineered vesicular stomatitis virus (VSV) with a SARS-CoV2 Spike protein. Using these models, we demonstrated that during inflammation cytoplasm of MSCs transferred to lung macrophages to decrease activation and the expression of MHC-II. Further, MSCs prevented a decrease in resident alveolar macrophages, suppressed proinflammatory macrophages, and blocked an influx in infiltrating monocytes (Fig 1). Depleting p-bodies from MSCs abolished the beneficial effects, despite transfer cytoplasmic component to macrophages at similar levels of control MSCs. Overall, our data suggest a novel form of cell communication that could explain how MSCs could lead to long-term beneficial effects on lung inflammation despite being rapidly cleared. [Figure presented]
ABSTRACT
Purpose: This paper analyzes how three major industrial stock indices related to South Korean real estate industries are affected by the exogenous shock of the measures taken to control COVID-19, coupled with investor sentiment, which has global impacts. Design/methodology/approach: The paper uses daily stock market indices on three major stock price indices: construction industry sector index, real estate operating company (REOC) industry index and the real estate investment trust (REIT) industry index of the Korea Stock Exchange (KRX), from January 8, 2020, when the World Health Organization (WHO) began to issue official indicators regarding COVID-19, to March 27, 2020, the last trading day of the week during which the South Korean government's stock market stabilisation fund was launched. Findings: Results indicate the REIT sector's stock rate of return to be relatively less sensitive to impacts of COVID-19 compared to those of the two other indices. Impulse response analysis also shows similar results. Impulse response estimations indicate that earlier information of REITs has prominent significance in explaining changes in the time series process itself. Similar to findings of prior studies that have been conducted with long-term perspectives, results of our short-term study indicate that the medium-risk, medium-return characteristic of the real estate industry has significance even in short-term perspectives. Practical implications: REITs can be an investment vehicle that provides strong benefits of diversified investment for mutual fund investment managers even in the case of short-term exogenous market disruptions. Originality/value: The analysis run in the empirical exercise is the first to consider the sensibility between international stock exchanges to the effects of measures taken to control COVID-19 impact. © 2021, Emerald Publishing Limited.
ABSTRACT
In this volatile post-COVID environment where customers look for ways to order products online using personal computers and mobile devices, a traditional sale/delivery of products via single distribution channel needs to be reassessed. As a revolutionary alternative to a conventional distribution channel, this paper proposes an omni-channel strategy. The omni-channel aims to maximize the customer shopping experience by diversifying and integrating the product purchase and delivery media through customer engagement. The omni-channel also facilitates the sales of products by allowing customers to seamlessly interact with retailers across the multiple channels such as websites, social media, brick-and-mortar stores, kiosks, call centers, and the like. Since the transformation of product sale, purchase, and delivery processes requires a new business mindset and innovative strategic initiatives, this paper sheds light on potential challenges and opportunities of implementing the omni-channel strategy, while identifying key success factors for the application of the omni-channel concept to e-tailing.
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Despite remarkable developments in healthcare, the world was not ready to stop the spread of the novel COVID-19 pandemic almost a century after the great influenza pandemic. The explosive increase in the number of patients stalled the healthcare system, and the first and apparent issue was the shortage of personal protective equipment (PPE). Our group established a system using a hydrogen peroxide vaporization method to decontaminate and reuse N95 respirators for healthcare workers. The system decontaminated over 12,000 units of PPE to cover institutions in West Texas. This service provided support at the most needed time during the pandemic.
ABSTRACT
In the post-epidemic era, big data technology has a wide range of applications in public crisis management. Especially for the 'new coronary pneumonia 'epidemic prevention and control, big data technology has shown a wide range of application potential. This paper explores the influence of big data on public crisis management in the post-epidemic era by combing the inherent attributes of big data and the principles of public crisis management in the post-epidemic era, analyzes the existing problems of epidemic prevention and control from the aspect of big data application, and gives suggestions for using big data to carry out epidemic prevention and control, and provides reference for the improvement of public crisis management system in the post-epidemic era. © 2021 IEEE.
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The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, it is imperative that each country monitors inbound travelers. Moreover, given that resources for quarantine are often limited, they must be carefully allocated. In this paper, to aid in such allocation by predicting the number of inbound COVID-19 cases, we propose Hi-COVIDNet, which takes advantage of the geographic hierarchy. Hi-COVIDNet is based on a neural network with two-level components, namely, country-level and continent-level encoders, which understand the complex relationships among foreign countries and derive their respective contagion risk to the destination country. An in-depth case study in South Korea with real-world COVID-19 datasets confirmed the effectiveness and practicality of Hi-COVIDNet. © 2020 ACM.