Information dissemination across global markets during the spread of COVID-19 pandemic☆
Ontology highlight
ABSTRACT:
SUBMITTER: Tripathi A
PROVIDER: S-EPMC9759749 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
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Project description:Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.
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Project description:The COVID-19 pandemic has caused severe disruption worldwide. We analyze the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate our model's performance in capturing periods of significant regime change, and provide weekly forecasts that improve risk management and investment decisions. An investment strategy that uses out-of-sample forecasts for market states outperforms a buy and hold strategy during the pandemic by a wide margin, both in terms of annualized returns and Sharpe ratios.
| S-EPMC8450758 | biostudies-literature
Project description:COVID-19 has given rise to a surge in the number of policy instruments used to deal with the pandemic at different levels of governments globally. While much attention has been placed on travel bans, lockdown, social distancing, and economic stimulus packages, government dissemination of epidemic information as a policy instrument has received less attention. Based on 14,637 news items collected from the portals of 79 municipal governments in China, this study aims to 1) conduct a content analysis of news items and construct three key attributes of governments' practices of epidemic information dissemination, namely, comprehensiveness, responsiveness, and the protection of privacy, and 2) study the patterns and determinants of the dissemination of epidemic information. Our results show that these cities vary substantially in how they disseminate statistical data and information on individual cases of COVID-19 infections within their localities, which are shaped by government performance in open data, severity of the pandemic, cities' administrative level, population, and health sector capacities. The findings generate theoretical and policy implications for government dissemination of epidemic information.
| S-EPMC8891011 | biostudies-literature
Project description:BackgroundThe 2020-2021 COVID-19 pandemic has added to the mental health strain on individuals and groups across the world in a variety of ways. Viral mitigation protocols and viral spread affect people on all continents every day, but at widely different degrees. To understand more about the mental health consequences of the pandemic, it is important to investigate whether or how people gather pandemic-related information and how obtaining this information differentially affects individuals.ObjectiveThis study aimed to investigate whether and to what extent higher levels of COVID-19-related media consumption across information sources are associated with the symptoms of anxiety, health anxiety, and depression, and whether and to what extent using social media and online interactive platforms versus traditional media platforms is associated with the symptoms of anxiety, health anxiety, and depression. Additionally, we aimed to investigate whether and to what extent avoidance of COVID-19-related information is associated with the aforementioned symptoms.MethodsIn a cross-sectional preregistered survey, 4936 participants responded between June 22 and July 13, 2020. Eligible participants were adults currently residing in Norway and were thus subjected to identical viral mitigation protocols. This sample was representative of the Norwegian population after utilizing an iterative raking algorithm to conduct poststratification. As 2 subgroups (transgender and intersex individuals) were too small to be analyzed, the final sample for descriptive statistics and regressions included 4921 participants. Multiple regressions were used to investigate associations between the symptoms of psychopathology and COVID-19-related information dissemination. Part correlations were calculated as measures of the effect size for each predictor variable. Due to the large anticipated sample size, the preregistered criterion for significance was set at P<.01.ResultsThe symptoms of anxiety and health anxiety were significantly associated with obtaining information from newspapers (P<.001), social media (P<.001), and the broader categories of online interactive (P<.001) and traditional media (P<.001). The symptoms of depression were significantly associated with obtaining information from newspapers (P=.003), social media (P=.009), and the broader category of online interactive media (P<.001). Additionally, avoidance of COVID-19-related information showed a significant association in all 3 domains of psychopathological symptoms (anxiety and depression, P<.001; health anxiety, P=.007).ConclusionsThis study found significant associations between the symptoms of psychopathology and the use of media for obtaining information related to the COVID-19 pandemic. Significant findings for obtaining information through newspapers, social media, and online interactive media were seen across all 3 measures of psychopathology. Avoidance of COVID-19-related information and associations with the symptoms of psychopathology emerged as core findings, with generally higher effect sizes compared with information attainment.Trial registrationClinicalTrials.gov NCT04442360; https://clinicaltrials.gov/ct2/show/NCT04442360.
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Project description: Not available
| S-BSST1055 | biostudies-other
Project description:Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.
| S-EPMC7390340 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
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Project description:This paper studies the connectedness among energy equity indices of oil-exporting and oil-importing countries around the world. For each country, we construct time-varying measures of how much shocks this country transmits to other countries and how much shocks this country receives from other countries. We analyze the network of countries and find that, on average, oil-exporting countries are mainly transmitting shocks, and oil-importing countries are mainly receiving shocks. Furthermore, we use panel data regressions to evaluate whether the connectedness among countries is influenced by economic sentiment, uncertainty, and the global COVID-19 pandemic. We find that the connectedness among countries increases significantly in periods of uncertainty, low economic sentiment, and COVID-19 problems. This implies that diversification benefits across countries are severely reduced exactly during crises, that is, during the times when diversification benefits are most important.
| S-EPMC9059158 | biostudies-literature
Project description:This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.
| S-EPMC4119747 | biostudies-other
Project description:Abstract 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.
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