The Geopolitical Consequences of COVID-19: Assessing Hawkish Mass Opinion in China.
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ABSTRACT:
SUBMITTER: Byun J
PROVIDER: S-EPMC8661655 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
The Geopolitical Consequences of COVID-19: Assessing Hawkish Mass Opinion in China.
Byun Joshua J Kim D G DG Li Sichen S
Political science quarterly 20210923 4
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Project description: Not available
| S-BSST1076 | biostudies-other
Project description:The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO2 emissions declines into firm climate action.
| S-EPMC9482555 | biostudies-literature
Project description:The pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has garnered the attention of scientists worldwide in the search for an effective treatment while also focusing on vaccine development. Several drugs have been used for the management of coronavirus disease 2019 (COVID-19), which has affected many hospitals and health centers worldwide. Statistically significant results are lacking on the effectiveness of the experimented drugs in reducing COVID-19 morbidity or mortality, as there are very few published randomized clinical trials. Despite this, the literature offers some material for study and reflection. This opinion review attempts to address three burning questions on COVID-19 treatment options. (1) What kind of studies are currently published or ongoing in the treatment of patients with COVID-19? (2) What drugs are currently described in the literature as options of treatment for patients affected by the infection? And (3) Are there specific clinical manifestations related to COVID-19 that can be treated with a customized and targeted therapy? By answering these questions, we wish to create a summary of current COVID-19 treatments and the anti-COVID-19 treatments proposed in the recent clinical trials developed in the last 3 mo, and to describe examples of clinical manifestations of the SARS-CoV-2 infection with a cause-related treatment.
| S-EPMC7520874 | biostudies-literature
Project description:We constructed a coronavirus disease community vulnerability index using micro district-level socioeconomic and demographic data and analyzed its correlations with case counts across the 3 pandemic waves in Hong Kong, China. We found that districts with greater vulnerability reported more cases in the third wave when widespread community outbreaks occurred.
| S-EPMC8237886 | biostudies-literature
Project description:A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.
| S-EPMC9411104 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description: Not available
| S-EPMC7159085 | biostudies-literature
Project description:To curb the spread of the coronavirus, China implemented lockdown policies on January 23, 2020. The resulting extreme changes in human behavior may have influenced the air pollutants concentration. However, despite these changes, hazy weather persisted in Shanghai and became a public issue. This study aims to investigate air pollutant mass concentration changes during the lockdown in Shanghai. Air pollutant mass concentration data and meteorological data during the pre-lockdown period and the level I response lockdown period were analyzed by statistical analysis and a Lagrangian particle diffusion model. The data was classified in three periods: P1 (pre-lockdown: 10 days before the Spring Festival), P2 (the first 10 days after lockdown: during the Spring Festival celebration), and P3 (the second 10 days after lockdown: after the Spring Festival). Data for the same period in 2019 were used as a reference. The results indicate that the Spring Festival holiday in 2019 resulted in a reduction in energy consumption, which led to a decrease in PM2.5 (26.4%) and NO2 (43.41%) mass concentration, but an increase in ozone mass concentration (31.39%) in P2 compared with P1. The integrated effect of the Spring Festival holiday and lockdown in 2020 resulted in a decrease in PM2.5 (36.5%) and NO2 (51.9%) mass concentrations, but an increase in ozone mass concentration (43.8%) in P2 compared with P1. After the Spring Festival, the mass concentrations of PM2.5, SO2, and NO2 increased by 74.41%, 5.52%, and 53.28%, respectively in P3 compared with P2 in 2019. However, PM2.5 and SO2 concentrations in 2020 continued to decrease, by 14.74% and 4.61%, respectively, while NO2 mass concentration increased by 7.82% in P3 compared with P2. We also found that PM2.5 mass concentration is susceptible to regional transmission from the surrounding cities. PM2.5 and other gaseous pollutants show different correlations in different periods, while NO2 and O3 always show a strong negative correlation. The principal components before the Spring Festival in 2019 were O3 and NO2, and after the Spring Festival, they were PM2.5 and CO, while the principal components before the lockdown in 2020 were PM2.5 and CO, and during lockdown they were O3 and NO2.
| S-EPMC7576102 | biostudies-literature
Project description:ObjectivesThe current COVID-19 outbreak in conjunction with the need to provide safe dental treatments and the limited knowledge on the efficacy of protective measures has posed dentists into a challenging situation. Therefore, the present article aimed at collecting experiences and recommendations of frontline clinical experts on critical aspects of dental treatment provision during pandemic.Material & methodsFrom a total of 32 European countries, one leading academic expert in Oral and Maxillofacial Surgery or Oral Surgery per country was asked to participate in an anonymous online 10-item survey on COVID-19 covering the topics of safety of dental settings, personal protective equipment (PPE), and patient-related measures to reduce transmission risk. Data collection took place from April 12th to May 22nd, 2020.ResultsA total of 27 experts from different European countries completed the survey. The transmission risk of SARS-CoV-2 in dental settings for aerosol-generating procedures was considered high by all experts except two. For aerosol-free and aerosol-generating procedures, more than 80% of the experts recommended face protection and caps for every single treatment. For aerosol-generating procedures, additional measures (FFP2/FFP3 masks and gowns) were suggested by the vast majority of the experts. To reduce transmission risk, all experts recommended limiting aerosol-generating procedures and reducing the number of patients in waiting areas as well as hand hygiene for the patients.ConclusionThe limitation of aerosol-generating procedures along with the usage of adequate personal protection equipment was considered to be crucial to protect dental healthcare providers and patients, thus reducing the transmission risk of COVID-19.
| S-EPMC7675432 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic.ObjectiveMining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion.MethodsFirst, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model.ResultsThe experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive.ConclusionsThe proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.
| S-EPMC10833090 | biostudies-literature