International currency markets and the COVID‐19 pandemic

We find that quantifying COVID‐19 pandemic shocks is critical to understanding international currency market returns. Scaled by population, shocks from between‐country differences in the number of weekly COVID‐19 deaths are informative in predicting exchange rate returns. Following Alfaro et al. (2020), we estimate the expected number of COVID‐19 deaths based on an exponential model and use it to construct two pandemic shocks that measure the unanticipated number of deaths on a weekly basis and the time‐varying correction of forecast provided new information from the previous week. We document negative impacts of COVID‐19 propagation on currency returns. In addition, we find that the government response, in particular fiscal and monetary stimulus packages, can help mitigate negative effects of COVID‐19 on currency returns. Our findings are robust to country‐specific pandemic measures, window sizes of the exponential model, and the choice of forecast model.

Dual Multiscale Mean Teacher Network for Semi-Supervised Infection Segmentation in Chest CT Volume for COVID-19

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating coronavirus 2019 (COVID-19). However, there are still some challenges for developing AI system: 1) most current COVID-19 infection segmentation methods mainly relied on 2-D CT images, which lack 3-D sequential constraint;2) existing 3-D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3-D volume;and 3) the emergent breaking out of COVID-19 makes it hard to annotate sufficient CT volumes for training deep model. To address these issues, we first build a multiple dimensional-attention convolutional neural network (MDA-CNN) to aggregate multiscale information along different dimension of input feature maps and impose supervision on multiple predictions from different convolutional neural networks (CNNs) layers. Second, we assign this MDA-CNN as a basic network into a novel dual multiscale mean teacher network ((DMT)-T-2-Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multiscale information. Our (DMT)-T-2-Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multiscale consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from multiple predictions of MDA-CNN. Third, we collect two COVID-19 segmentation datasets to evaluate our method. The experimental results show that our network consistently outperforms the compared state-of-the-art methods.

Detection of Antibody versus Antigen, Optimal Option of Different Serological Assays Based Tests for COVID-19 Diagnosis: A Meta-Analysis

Background: In this study, the diagnostic efficacy of antigen test and antibody test were assessed. Additional-ly, the difference of sensitivity, specificity, and diagnostic odds ratio were compared concerning efficacy of antibody test versus antigen test for Corona Virus Disease 2019 (COVID-19) diagnosis. Methods: Online databases were searched for full-text publications and STATA software was used for data pooling and analysis before Sep 1st, 2022. Forrest plot was used to show the pooled sensitivity, specificity and diagnostic odds ratio. Combined receiver operating characteristic (ROC) curve was used to show the area of under curve of complex data. Results: Overall, 25 studies were included. The sensitivity (0.68, 95% CI: 0.53-0.80) and specificity (0.99, 95% CI: 0.98-0.99) in antibody or antigen was calculated. The time point of test lead to heterogeneity. The area under curve (AUC) was 0.98 (95% CI: 0.96-0.99), and the diagnostic odds ratio (DOR) was 299.54 (95% CI: 135.61-661.64). Subgroup analysis indicated antibody test with sensitivity (0.59, 95% CI: 0.44-0.73) and specificity (0.98, 95% CI: 0.95-0.99) and antigen test with sensitivity of 0.77 (95% CI: 0.53-0.91) and specificity of 0.99 (95% CI: 0.98-1.00). Higher AUC and DOR were proved in antigen test. Conclusion: The present study compared the efficacy of antibody test versus antigen test for COVID-19 di-agnosis. Better diagnostic efficacy, lower heterogeneity, and less publication bias of rapid antigen testing was suggested in this study. This study would help us to make better strategy about choosing rapid and reliable testing method in diagnosis of the COVID-19 disease. © 2023 Fu et al. Published by Tehran University of Medical Sciences.

Knowledge Map of Chinese Tourists Travelling to Thailand: Based on the Bibliometric Method

Chinese tourists have essential economic and cultural exchange values for Thai tourism. The present research on Chinese tourists traveling to Thailand (CTTT) has made a significant breakthrough, but it rarely observes the research status quo, hot spots, and the future based on bibliometric methods and visualization technology. Based on 710 articles on the Web of Science (WoS), Cite Space visualization technology was used to analyze the publishing trend of CTTT, authors, institutions, countries, keywords, and categories, and build a knowledge map about CTTT. The study found that although the positive trend of published articles indicates excellent future development, the overall number of published articles is not ideal. Moreover, research on this topic has made significant progress and breakthroughs in the healthcare field, meaning that there is still much research space in the business and economic areas. Thai institutions and scientific research institutions and their scholars have significantly contributed to CTTT. They focus more on healthcare, disease, health, sexual safety, and equality in travel. Although there is an increasing focus on consumer behavior, habits, and models, However, the research on CTTT under the background of the digital economy, sharing economy, big data, artificial intelligence, and the COVID-19 pandemic is of great research value. Therefore, this study encourages different countries, institutions, and scholars to do more on this topic. Furthermore, interdisciplinary research is even more critical. © 2022.

Changes in operational risk and its determinants under Covid-19

This examines the direct impact of the Covid-19 pandemic on the operational risk of Chinese commercial banks and the moderating effect of bank size, business diversification and regulatory records. To address the lack of data in operational risk studies, we gather financial statements on operational risk to obtain empirical proxy variables. We conduct an empirical study using 639 financial statements from 20 listed commercial banks in China from 2011 Q4 to 2021 Q3 and find that the Covid-19 pandemic has increased the operational risk of commercial banks. Moreover, business diversification, bank size and poor regulatory record significantly increase the operational risk effects of the pandemic. Finally, we test the robustness of our results, supporting our conclusions and providing new insights into the interaction between the Covid-19 pandemic and banks’ operational risk. © 2022 Infopro Digital Risk (IP) Limited.

Toward a new, collaborative global financing architecture for fragile, low, and middle-income countries' data priorities

The COVID-19 pandemic has occurred against a sobering global backdrop: national data collection programs and the production of core economic statistics have long been under-funded (by national government as well as the international development community), and data gaps are still significant. The pandemic has highlighted the importance of NSOs and the urgent need to strengthen and modernize core data collection programs as the backbone of national data systems. As the severity of this problem and its damaging implications are becoming more salient, members of the international development and national statistics communities have joined forces in an effort to address it. A collective, high-level effort is now being mobilized by senior leadership of the World Bank and the United Nations, in close collaboration with the Global Partnership for Sustainable Development Data, to join forces to increase global investments in fragile, low-and-middle-income countries' data priorities and to better put data to work for green, resilient, inclusive development. Specifically, two new complementary funds have recently been launched by the World Bank and United Nations to support countries' data systems, data capital, and risk analytics in a coordinated way: the World Bank-hosted Global Data Facility and the UN-hosted Complex Risk Analytics Fund (CRAF'd). © 2022 - The authors.

On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction. © 2022 IEEE.

Xi'an network attention measurement in the post-epidemic era

Based on Baidu index data and the development timeline of COVID-19 in China, this study analyzes the spatial and temporal distribution pattern of network attention in Xi'an under epidemic prevention and control. The results show that: 1) In 2020, the network attention of Xi ' an affected by the epidemic is low. The trend of monthly network attention in the year is consistent with the time axis of domestic epidemic development, showing a ' double peak and double valley ' mode, and it is high in summer and autumn, and low in winter and spring. On the holidays, the attention increased before the festival, and the ' May 1 ' reached the peak one day before the festival, and the ' Eleventh ' reached the peak on the third day of the festival, showing a clear ' blowout ' trend. 2) The spatial distribution of Xi'an network attention is scattered, and shows the characteristics of high network attention in Henan, Sichuan and other surrounding provinces and Guangdong, Jiangsu, Zhejiang and other coastal economic developed areas. © COPYRIGHT SPIE.

Eye-tracking study of public acceptance of 5G base stations in the context of the COVID-19 pandemic

Purpose This study applied eye-tracking techniques and questionnaires within the framework of the Stimulus-Organism-Response Model (SOR) and Technology Acceptance Model (TAM), to investigate the influencing factors of the public acceptance of 5G base stations. Design/methodology/approach This study used a combination of eye-tracking experiments and questionnaires. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings (1) The Technology Acceptance Model (TAM) could be used to explain the effects on public acceptance of 5G base stations in the context of the COVID-19 pandemic. The public's perceived usefulness and ease of use of 5G base stations positively affects public acceptance of 5G base stations. (2) The public's perceived risk of 5G base stations has a negative influence on the public acceptance of 5G base stations. (3) The public's visual attention to the different valence information about 5G base stations positively impacts the perceived ease of use while having negative impacts on perceived risk. (4) Visual attention to various valence information of 5G base stations can indirectly influence public acceptance through the perceived risk. Originality/value Applying the SOR and TAM to data obtained from eye-tracking experiments and questionnaires, this study analyzed the factors and mechanisms influencing public acceptance of 5G base stations in the context of the COVID-19 pandemic.

First contact: A series of zoom-based, virtual on call shifts

Aim: COVID-19 has resulted in reduced exposure to on-call shifts where medical students could increase confidence and proficiency in task prioritisation and decision making. Existing 'simulated on-calls' provide a substitute in a controlled environment, however in person teaching has also been limited by COVID-19. Our virtual on-call sessions use ZOOM to replicate the higher-level learning experiences normally conferred by live simulation. Method: We designed a series of virtual 'on-calls' for medical students. Participants were 'on-call', receiving 'bleeps' which were 'answered' by calling a facilitator via ZOOM. The facilitator would roleplay a scenario and the 'Electronic Patient Record' (EPR) on Google Forms contained patient notes and observations. Students needed to collect information from the facilitator and document a management plan into the EPR. Participants received 'bleeps' of varying complexity, urgency and relevance and were expected to prioritise and triage tasks accordingly. Evaluation was via a pre/post session quiz with separate feedback forms. Results: 23 students from 18 universities participated. Students reported increased confidence in managing on-call scenarios, and average scores improved in the post session quiz. Positive feedback was paid to the variety of scenarios, the EPR system and the feeling of realism elicited from the need to triage and prioritise jobs. Conclusions: Our framework uses readily accessible technology to provide interactive learning experience. Feedback suggested students engaged in higher order learning and thinking, achieving our stated aims. We aim to incorporate technologies such as automation software which will allow for a scalable, free, and accessible virtual on call.

Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System

COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User's confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1] [2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems. We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan. IEEE

Thermally Activated Charge Transfer in Dual-Emission Mn2+-Alloyed Perovskite Quantum Wells for Luminescent Thermometers

Owing to the pandemic of Coronavirus disease 2019 (COVID-19), the demands on ultracold-chain logistics have rapidly increased for the storage and transport of mRNA vaccines. Herein, we report a soluble luminescent thermometer based on thermally activated dual-emissions of Mn2+-alloyed 2D perovskite quantum wells (QWs). Owing to the Mn2+ alloying, the binding energy of perovskite QW exciton is reduced from 291 to 100 meV. It facilitates the dissociation of excitons into free charge carriers, which are then transferred and trapped on Mn2+. The temperature-dependent charge transfer efficiency can be tuned from 8.8% (-93 °C) to 30.6% (25 °C), leading to continuous ratiometrical modulation from exciton-dominated violet emission to Mn2+-dominated orange emission. The highest sensitivity (1.44% per K) is approximately twice that of the Mn2+-doped chalcogenide quantum dots. Taking advantage of highly reversible color switching, Mn2+-alloyed QWs provide an economical solution to monitor the ultracold-chain logistics of the COVID-19 vaccine. © 2022 American Chemical Society.

Estimating the number of undetected COVID-19 cases among travellers from mainland China

Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China. © 2021 Bhatia S et al.

Estimating the number of undetected COVID-19 cases among travellers from mainland China

Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China.

Eltrombopag plus rhtpo versus eltrombopag for itp during the covid-19 pandemic (elaborate-19)

Background: Immune thrombocytopenia (ITP) is an acquired thrombocytopenia caused by immune-mediated platelet destruction and impaired platelet production. Recombinant human thrombopoietin (rhTPO) and Eltrombopag, a small molecule agonist of thrombopoietin receptor (TPO-RA), are both recommended as the subsequent treatment for ITP patients, which also already showed robust efficacy. They increase the number of platelets through different mechanisms, and previous studies demonstrated that they might exert synergic effect. During the COVID- 19 pandemic, the classical subsequent treatment regimen for ITP of immunosuppressants and/or steroids might increase patients' susceptibility of virus infections. The investigators hypothesized that the combination of these two agents could be a promising option for ITP treatment during the COVID-19 pandemic. To minimize ITP patients' risk during the COVID-19 global crisis and to improve treatment efficacy, this treatment regimen of Eltrombopag plus rhTPO should be investigated. This trial is registered with ClinicalTrials.gov, number NCT01667263. Aims: This study aimed to evaluate the sustained responses at 6 months and safety of Eltrombopag plus rhTPO as treatment for corticosteroid- resistant or relapsed ITP patients during the COVID-19 pandemic. Methods: In this open-label, randomized, phase 2 trial, we enrolled confirmed corticosteroid-resistant or relapsed adult ITP patients from 5 different tertiary medical centers in China. They were randomly assigned 1:1 with an interactive web-based response system to receive either Eltrombopag 25-75 mg oral daily according to platelet response plus rh-TPO 300U/kg subcutaneous injection once daily for 7 consecutive days, followed by a tapering dose in maintenance therapy or Eltrombopag monotherapy for 12 weeks (Figure 1). The primary endpoint was 6-month sustained response (SR) defined as platelet counts maintained > 30×109/L and at least a doubling of baseline platelet count. Key secondary endpoints were initial response by day 14, duration of response (DOR), TTR, bleeding scores, and adverse events (AEs). Results: Between August 2020, and March 2021, 60 patients were randomly allocated into either rh-TPO plus Eltrombopag (n=30) or Eltrombopag monotherapy (n=30). At the 6-month follow-up, the proportion of patients with SR was significantly higher in the rh-TPO plus Eltrombopag group than in the Eltrombopag monotherapy group (66.7% vs 36.7%, p= 0.020). The combination of rh-TPO and Eltrombopag resulted in a higher incidence of initial response by day 14 compared with Eltrombopag monotherapy (76.7% vs 60%, p= 0.165). Throughout the follow-up period, overall DOR was greater in the combination group, estimated by the Kaplan-Meier analysis. Bleeding was more effectively controlled in the rh-TPO plus Eltrombopag arm, with fewer bleeding events and lower bleeding scores. There was no difference between the 2 groups in terms of rescue treatments. All subjects tolerated the treatment well, and no grade 4 adverse events or treatment- related death were reported. No statistically significant differences were observed in the incidence of treatment-related AEs between the two groups. Summary/Conclusion: Rh-TPO plus Eltrombopag is an effective and safe treatment for corticosteroid-resistant or relapsed ITP patients during the COVID-19 pandemic. (NCT04516837).

Erratum: Study on clinical characteristics of 173 cases of COVID-19 and effect of glucocorticoid on nucleic acid negative conversion

[This corrects the article on p. 4251 in vol. 13, PMID: 34150012.].