An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors.

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Do the green bonds overreact to the COVID-19 pandemic?

This paper explores the impacts of the COVID-19 pandemic on the global green bond and conventional assets, including commodity, treasury, stock and clean energy markets, using Diebold and Yilmaz (2012) and Baruník and Krehlík, 2018b spillover framework. The results show that spillover transmitted from COVID-19 is relatively strong over a medium- and long-term horizon, and the spillover effects sharply increased when the pandemic became severe. Furthermore, green bonds are most affected by the COVID-19 pandemic, followed by the treasury, while the other conventional assets are only slightly affected. Additionally, our findings also contain a low-risk portfolio during COVID-19 pandemic.

Chinese medicine for residual symptoms of COVID-19 recovered patients (long COVID)-A double-blind, randomized, and placebo-controlled clinical trial protocol.

Introduction: Coronavirus disease 2019 (COVID-19) is the current global pandemic of which residual symptoms exhibited by post-acute, rehabilitating patients include fatigue, dyspnoea, and insomnia. Chinese medicine (CM) has been widely used in China to treat different stages of COVID-19. While there are a significant number of clinical studies suggesting its efficacy and safety in its use during acute stage, there are very few randomized controlled trials focusing on the rehabilitation stage. Liujunzhi Decoction and Shashen Maidong Decoction are frequently recommended by official clinical guidelines in China to treat COVID-19 patients in rehabilitation stage. This double-blind, randomized, placebo controlled study aims to evaluate the efficacy and safety of the combination of the two formulae [named "COVID-19 Rehab Formula (CRF)"] in treating COVID-19 residual symptoms (long COVID). Methods: Eligible subjects will be randomly divided into treatment group and control group in 1:1 ratio. Treatment group will receive CRF along with certain pre-defined CM according to symptoms for 8 weeks, while control group will receive equivalent packs of placebo for 8 weeks. Data in terms of Fatigue Severity Score (FSS), self-reported COVID-19 long term symptom assessment, the modified British Medical Research Council (mMRC) Dyspnoea Scale, EuroQol Five-Dimension Five-Level (EQ-5D-5L) Questionnaire, pulmonary function test and adverse events will be collected and analyzed by SPSS 24. Blood test on liver and renal functions will also be conducted as safety measures. Conclusion: This study will evaluate the efficacy and safety of CRF in the treatment COVID-19 residual symptoms in a scientifically rigorous design. Clinical trial registration: [ClinicalTrials.gov], identifier [NCT04924881].

Extreme quantile spillovers and drivers among clean energy, electricity and energy metals markets

This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they increase dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers.

Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach.

The COVID-19 pandemic has led to extensive news coverage, causing investor sentiment to swing, which has further increased financial market price volatility. There is an increasing need to find a hedge against sentiment risk. This paper examines the hedge capabilities of gold and Bitcoin against COVID-19-related news sentiment (CNS) risk under a nonlinear autoregressive distributed lag (NARDL) model. Our empirical results reveal that there is an obvious asymmetric effect from the CNS on gold prices in the short run and that the decrease in the COVID-19-related news index would have a greater impact on gold prices than when it increases. The impact of CNS on Bitcoin prices is asymmetric in the long and short term, especially in the long term. In addition, we conclude that gold is a hedge against CNS risk in the long term, and the hedging effect of Bitcoin is mainly reflected in the short-term.

Chemo- and Site-Selective Lysine Modification of Peptides and Proteins under Native Conditions Using the Water-Soluble Zolinium.

Site-selective lysine modification of peptides and proteins in aqueous solutions or in living cells is still a big challenge today. Here, we report a novel strategy to selectively quinolylate lysine residues of peptides and proteins under native conditions without any catalysts using our newly developed water-soluble zoliniums. The zoliniums could site-selectively quinolylate K350 of bovine serum albumin and inactivate SARS-CoV-2 3CLpro via covalently modifying two highly conserved lysine residues (K5 and K61). In living HepG2 cells, it was demonstrated that the simple zoliniums (5b and 5B) could quinolylate protein lysine residues mainly in the nucleus, cytosol, and cytoplasm, while the zolinium-fluorophore hybrid (8) showed specific lysosome-imaging ability. The specific chemoselectivity of the zoliniums for lysine was validated by a mixture of eight different amino acids, different peptides bearing potential reactive residues, and quantum chemistry calculations. This study offers a new way to design and develop lysine-targeted covalent ligands for specific application.

Realized higher-order moments spillovers between commodity and stock markets: Evidence from China

Using 5-min data of Chinese stock market index and eight Chinese commodity futures (soybean, wheat, corn, gold, silver, copper and aluminum, crude oil) from March 26, 2018 to October 22, 2020, we analyze the dynamic spillover connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, during various shock periods via a time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The results show that spillover effects between stock and commodity markets intensify during shock periods such as ‘Trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger;however, higher-order moment spillovers contain additional information of stock-commodity spillovers that cannot be observed from volatility spillovers. Shocks from the silver market influence all three realized moments of the entire financial markets. Soybean, corn, aluminum, and oil markets are easily affected by other markets. The contribution of wheat to the system of spillovers between stock and commodity markets is only observed at higher-order moments. Further analyses involving OLS and quantile regressions show that total spillovers are generally affected by the US stock market and economic uncertainties as well as the COVID epidemic. We construct daily realized volatility, skewness, and kurtosis using 5-min data of eight Chinese commodity futures and the Chinese stock market index from March 26, 2018 to October 22, 2020, then analyse the dynamic spillovers of realized moments among these markets. The results show that the spillover effects between commodity and stock markets intensify during shock periods such as ‘trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger than spillovers in skewness or spillovers in kurtosis;however, spillovers in higher-order moments seem to contain additional information. Shocks from the silver market influence realized moments of other markets. Soybean, corn, aluminium, and oil markets are affected by other markets. The contribution of wheat as a net transmitter to the system of spillovers between stock and commodity markets is only observed at higher-order realized moments. The results from OLS and quantile regressions show that the total spillovers are generally affected by the US stock market, economic uncertainties, and the COVID-19 outbreak.

Early Cyberspace Emergency Response by Predicting Social-Emotional Security

With the intensified social conflicts and cyberspace crises, the public is facing the emotional impact and lack of security feelings responding to emergencies. The most recent research only focuses on the influence of discrete emotions, but the induced stressful feeling of emotional security has not been a concern by the government. In this work, we first propose a concept of social-emotional security, evolving from the classical theories of risk society and psychological resilience. Second, we integrate a social-emotional security index measurement method with the proposed three metrics: emotional bias, situational risk, and potential hazard. We also suggest a grading scheme for the emotional regulation strategy with a 0.3 safety valve. Finally, the accuracy is over 78% for detecting the potential risk of emerging events, and the method is feasible in another 30 social safety events with a trend coincidence beyond 63.3%.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

Time-frequency spillovers among carbon, fossil energy and clean energy markets: The effects of attention to climate change

Carbon markets are closely connected to fossil energy and clean energy markets, but few studies focus on the size and direction of time-frequency spillovers among these markets and the role of climate change attention. Using the frequency-domain spillover index method and nonparametric causality-in-quantiles test, we explore the time-frequency spillovers among carbon, fossil energy and clean energy markets, and consider the casual effects of climate change attention. We find that the spillover effects among carbon, fossil energy and clean energy markets are time-varying, with short-term spillovers stronger than long-term spillovers. Carbon market is a net receiver of spillovers from the oil market and clean energy markets in the short term, but it becomes a net transmitter of spillovers to the coal and gas markets in the long term. Our marginal spillover effects analysis suggests that the COVID-19 pandemic has increased cross-market risk contagion in the long term and that carbon market bears larger input risks. Investors' attention to climate change has significant causal effects on the spillovers, and the causal impact of climate change attention on total spillover has significantly increased during the COVID-19 pandemic. Our findings provide important guidelines for investment in environmental protection and demonstrate the importance of formulating differentiated policies for environmental protection in different time horizons.

Comorbidity of hepatic cystic echinococcosis with HBV/HCV infection, liver cirrhosis, and hepatocellular carcinoma. (Theme Issue: New advances in the precise diagnosis and treatment of liver cancer.) [Chinese]

Objective: To investigate the comorbidity of hepatic cystic echinococcosis with HBV/HCV infection, liver cirrhosis, and hepatocellular carcinoma, and to lay a foundation for further research on the influence of hepatic cystic echinococcosis on HBV/HCV infection, liver cirrhosis, and hepatocellular carcinoma.

Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains

Many scholars have explored the COVID-19 impact on the crude oil, gold, and Bitcoin markets, whereas most have ignored the media coverage influence. This paper focuses on examining information spillover from epidemic-related news to the crude oil, gold, and Bitcoin markets with the time-frequency analysis method. The empirical results reveal that both the return and volatility spillovers from epidemic-related news to the crude oil, gold, and Bitcoin markets are stronger in the short term (less than 1 week). In the long term, only the media sentiment index notably impacts crude oil, gold, and Bitcoin market returns. The volatility spillover from media coverage to crude oil mainly occurs in the short term. Regarding the gold and Bitcoin markets, the long-term volatility spillovers are significant. An obvious risk contagion path is found. Media hype is the main risk transmitter and transmits vast shocks to these three markets, especially the Bitcoin market, which subsequently transmits these shocks to the gold market. Risk accumulates systemically in the gold and Bitcoin markets. These findings have crucial empirical implications for policymakers and investors when formulating related short- or long-term decisions during the pandemic.

Epidemiological features and spatial-temporal distribution of visceral leishmaniasis in mainland China: a population-based surveillance study from 2004 to 2019.

BACKGROUND: Although visceral leishmaniasis (VL) was largely brought under control in most regions of China during the previous century, VL cases have rebounded in western and central China in recent decades. The aim of this study was to investigate the epidemiological features and spatial-temporal distribution of VL in mainland China from 2004 to 2019. METHODS: Incidence and mortality data for VL during the period 2004-2019 were collected from the Public Health Sciences Data Center of China and annual national epidemic reports of VL, whose data source was the National Diseases Reporting Information System. Joinpoint regression analysis was performed to explore the trends of VL. Spatial autocorrelation and spatial-temporal clustering analysis were conducted to identify the distribution and risk areas of VL transmission. RESULTS: A total of 4877 VL cases were reported in mainland China during 2004-2019, with mean annual incidence of 0.0228/100,000. VL incidence showed a decreasing trend in general during our study period (annual percentage change [APC] = -4.2564, 95% confidence interval [CI]: -8.0856 to -0.2677). Among mainly endemic provinces, VL was initially heavily epidemic in Gansu, Sichuan, and especially Xinjiang, but subsequently decreased considerably. In contrast, Shaanxi and Shanxi witnessed significantly increasing trends, especially in 2017-2019. The first-level spatial-temporal aggregation area covered two endemic provinces in northwestern China, including Gansu and Xinjiang, with the gathering time from 2004 to 2011 (relative risk [RR] = 13.91, log-likelihood ratio [LLR] = 3308.87, P < 0.001). The secondary aggregation area was detected in Shanxi province of central China, with the gathering time of 2019 (RR = 1.61, LLR = 4.88, P = 0.041). The epidemic peak of October to November disappeared in 2018-2019, leaving only one peak in March to May. CONCLUSIONS: Our findings suggest that VL is still an important endemic infectious disease in China. Epidemic trends in different provinces changed significantly and spatial-temporal aggregation areas shifted from northwestern to central China during our study period. Mitigation strategies, including large-scale screening, insecticide spraying, and health education encouraging behavioral change, in combination with other integrated approaches, are needed to decrease transmission risk in areas at risk, especially in Shanxi, Shaanxi, and Gansu provinces.

COVID-19-related government interventions and travel and leisure stock

COVID-19-related government interventions have significantly affected tourism, while the impact of government interventions on the tourism financial market remains essentially unexplored. This paper comprehensively evaluates how COVID-19-related government interventions affected the travel and leisure stock markets based on a panel quantile regression model. Three government interventions (stringency index, containment and health index and economic support index) and two important stock market features (return and volatility) are discussed. The results reveal that the three government interventions are beneficial to the travel and leisure stock market, especially when the market is under adverse conditions. Specifically, containment and health measures lead to an increase in stock returns. Stringency measures and economic support measures promote stock return and restrain stock market volatility. This study provides significant insights for protecting and recovering the travel and leisure stock market by considering when and which government interventions should be implemented.

Characterization of SARS-CoV-2 worldwide transmission based on evolutionary dynamics and specific viral mutations in the spike protein.

BACKGROUND: The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is pandemic. However, the origins and global transmission pattern of SARS-CoV-2 remain largely unknown. We aimed to characterize the origination and transmission of SARS-CoV-2 based on evolutionary dynamics. METHODS: Using the full-length sequences of SARS-CoV-2 with intact geographic, demographic, and temporal information worldwide from the GISAID database during 26 December 2019 and 30 November 2020, we constructed the transmission tree to depict the evolutionary process by the R package "outbreaker". The affinity of the mutated receptor-binding region of the spike protein to angiotensin-converting enzyme 2 (ACE2) was predicted using mCSM-PPI2 software. Viral infectivity and antigenicity were tested in ACE2-transfected HEK293T cells by pseudovirus transfection and neutralizing antibody test. RESULTS: From 26 December 2019 to 8 March 2020, early stage of the COVID-19 pandemic, SARS-CoV-2 strains identified worldwide were mainly composed of three clusters: the Europe-based cluster including two USA-based sub-clusters; the Asia-based cluster including isolates in China, Japan, the USA, Singapore, Australia, Malaysia, and Italy; and the USA-based cluster. The SARS-CoV-2 strains identified in the USA formed four independent clades while those identified in China formed one clade. After 8 March 2020, the clusters of SARS-CoV-2 strains tended to be independent and became "pure" in each of the major countries. Twenty-two of 60 mutations in the receptor-binding domain of the spike protein were predicted to increase the binding affinity of SARS-CoV-2 to ACE2. Of all predicted mutants, the number of E484K was the largest one with 86 585 sequences, followed by S477N with 55 442 sequences worldwide. In more than ten countries, the frequencies of the isolates with E484K and S477N increased significantly. V367F and N354D mutations increased the infectivity of SARS-CoV-2 pseudoviruses (P < 0.001). SARS-CoV-2 with V367F was more sensitive to the S1-targeting neutralizing antibody than the wild-type counterpart (P < 0.001). CONCLUSIONS: SARS-CoV-2 strains might have originated in several countries simultaneously under certain evolutionary pressure. Travel restrictions might cause location-specific SARS-CoV-2 clustering. The SARS-CoV-2 evolution appears to facilitate its transmission via altering the affinity to ACE2 or immune evasion.

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China.

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.

Intelligent anti-epidemic mask based on KF and ECF fusion algorithm.

In response to environmental pollution and the spread of Coronavirus Disease 2019 (COVID-19), this paper proposes a new type of smart mask design, and specifically proposes an optimized double closed-loop control method, especially an improved filtering fusion algorithm. Using the filtering fusion algorithm proposed in this paper, after the Kalman filter (KF) filters the raw data of the attitude sensor, explicit complementary filtering and data fusion are used to obtain the attitude angle of the body. At the same time, the obtained attitude angle is combined with acceleration and blood oxygen concentration to obtain the behaviour characteristic value. On this basis, the speed of the oxygen supply fan captured by the photoelectric sensor is used to form a closed loop with the characteristic value of the behaviour. Finally, the structure of the mask is upgraded and optimized through fluid mechanics simulation, and experiments have verified that the combination of the replaceable filter cloth, the intelligent control system and the ultraviolet disinfection device can effectively protect people's health.

Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic.

The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of crude oil futures during the COVID-19 pandemic is examined in this research. We proposed a modeling framework, genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during the COVID-19 pandemic on the volatility of crude oil futures. GA-RFOS-ELM could learn block-by-block with fixed or varying block size when considering the block own valid period. The experimental results illustrate that news during the COVID-19 pandemic has more predictive information, which is crucial for short-term volatility forecasting of crude oil futures. The novel approach illustrates that online update learning ability is needed during the COVID-19 pandemic, which could be effective and efficient in volatility forecasting of crude oil futures. The contributions of our study are significant for investors and administrators to predict and understand the behavior of volatility during the COVID-19 pandemic.