Review and outlook of global energy use under the impact of COVID-19.

By collecting and sorting the energy demand data of developing and developed countries, this paper makes a comprehensive analysis of their energy demand, including the change of energy demand and the change trend of energy load in various sectors. The survey scope of the article includes the overall change trend of energy supply, natural gas, oil, electricity, coal, renewable energy (such as wind energy, solar energy, geothermal energy, tidal energy, etc.), and the data change of global carbon dioxide emission. Besides, this paper selects a variety of energy sources for comprehensive analysis to analyze the existing change trend in chronological order. The analysis methods include data statistics of primary energy production and consumption, energy intensity analysis of gross domestic product (GDP), production, and demand balance of oil, natural gas, and coal, and study the trade balance between different types of energy in different countries and regions. The regions examined in this review include the organization for economic cooperation and development (OECD); the group of seven (G7); Brazil, Russia, India, China and South Africa (BRICs); the European Union; Europe; North America; the Commonwealth of Independent States (CIS); Asia; Latin America; the Pacific Ocean; the Middle East and Africa. By studying these data, we can make a better summary of the current energy use, so as to conveniently grasp the context of energy development and have a general understanding of the current energy structure. Therefore, individuals and policymakers in the fields of energy trade can think more deeply about the future situation and draw conclusions.

Review and outlook of global energy use under the impact of COVID‐19

By collecting and sorting the energy demand data of developing and developed countries, this paper makes a comprehensive analysis of their energy demand, including the change of energy demand and the change trend of energy load in various sectors. The survey scope of the article includes the overall change trend of energy supply, natural gas, oil, electricity, coal, renewable energy (such as wind energy, solar energy, geothermal energy, tidal energy, etc.), and the data change of global carbon dioxide emission. Besides, this paper selects a variety of energy sources for comprehensive analysis to analyze the existing change trend in chronological order. The analysis methods include data statistics of primary energy production and consumption, energy intensity analysis of gross domestic product (GDP), production, and demand balance of oil, natural gas, and coal, and study the trade balance between different types of energy in different countries and regions. The regions examined in this review include the organization for economic cooperation and development (OECD);the group of seven (G7);Brazil, Russia, India, China and South Africa (BRICs);the European Union;Europe;North America;the Commonwealth of Independent States (CIS);Asia;Latin America;the Pacific Ocean;the Middle East and Africa. By studying these data, we can make a better summary of the current energy use, so as to conveniently grasp the context of energy development and have a general understanding of the current energy structure. Therefore, individuals and policymakers in the fields of energy trade can think more deeply about the future situation and draw conclusions. This review comprehensively analyzes the energy use and demand in developing countries and developed countries, and forecasts the future energy supply and demand in combination with the past development trend.

General Short-Term Load Forecasting Based on Multi-Task Temporal Convolutional Network in COVID-19

The spread of the global COVID-19 epidemic has resulted in significant shifts in electricity consumption compared to regular days. It is unknown if standard single-task, single-indicator load forecasting algorithms can accurately reflect COVID-19 load patterns. Power practitioners urgently want a simple, efficient, and accurate solution for anticipating reliable load. In this paper, we first propose a unique collaborative TCN-LSTM-MTL short-term load forecasting model based on mobility data, temporal convolutional networks, and multi-task learning. The addition of the parameter sharing layers and the structure with residual convolution improves the data input diversity of the forecasting model and enables the model to obtain a wider time series receptive field. Then, to demonstrate the usefulness of the mobility optimized TCN-LSTM-MTL, tests were conducted in three levels and twelve base regions using 19 different benchmark models. It is capable of controlling predicting mistakes to within 1% in the majority of tasks. Finally, to rigorously explain the model, the Shapley additive explanations (SHAP) visual model interpretation technology based on game theory is introduced. It examines the TCN-LSTM-MTL model's internal mechanism at various time periods and establishes the validity of the mobility indicators as well as the asynchronous relationship between indicator significance and real contribution.

Exploring the possible molecular targeting mechanism of Saussurea involucrata in the treatment of COVID-19 based on bioinformatics and network pharmacology.

OBJECTIVE: Based on bioinformatics and network pharmacology, the treatment of Saussurea involucrata (SAIN) on novel coronavirus (COVID-19) was evaluated by the GEO clinical sample gene difference analysis, compound-target molecular docking, and molecular dynamics simulation. role in the discovery of new targets for the prevention or treatment of COVID-19, to better serve the discovery and clinical application of new drugs. MATERIALS AND METHODS: Taking the Traditional Chinese Medicine System Pharmacology Database (TCMSP) as the starting point for the preliminary selection of compounds and targets, we used tools such as Cytoscape 3.8.0, TBtools 1.098, AutoDock vina, R 4.0.2, PyMol, and GROMACS to analyze the compounds of SAIN and targets were initially screened. To further screen the active ingredients and targets, we carried out genetic difference analysis (n = 72) through clinical samples of COVID-19 derived from GEO and carried out biological process (BP) analysis on these screened targets (P ≤ 0.05)., gene = 9), KEGG pathway analysis (FDR≤0.05, gene = 9), protein interaction network (PPI) analysis (gene = 9), and compounds-target-pathway network analysis (gene = 9), to obtain the target Point-regulated biological processes, disease pathways, and compounds-target-pathway relationships. Through the precise molecular docking between the compounds and the targets, we further screened SAIN's active ingredients (Affinity ≤ -7.2 kcal/mol) targets and visualized the data. After that, we performed molecular dynamics simulations and consulted a large number of related Validation of the results in the literature. RESULTS: Through the screening, analysis, and verification of the data, it was finally confirmed that there are five main active ingredients in SAIN, which are Quercitrin, Rutin, Caffeic acid, Jaceosidin, and Beta-sitosterol, and mainly act on five targets. These targets mainly regulate Tuberculosis, TNF signaling pathway, Alzheimer's disease, Pertussis, Toll-like receptor signaling pathway, Influenza A, Non-alcoholic fatty liver disease (NAFLD), Neuroactive ligand-receptor interaction, Complement and coagulation cascades, Fructose and mannose metabolism, and Metabolic pathways, play a role in preventing or treating COVID-19. Molecular dynamics simulation results show that the four active ingredients of SAIN, Quercitrin, Rutin, Caffeic acid, and Jaceosidin, act on the four target proteins of COVID-19, AKR1B1, C5AR1, GSK3B, and IL1B to form complexes that can be very stable in the human environment. Tertiary structure exists. CONCLUSION: Our study successfully explained the effective mechanism of SAIN in improving COVID-19, and at the same time predicted the potential targets of SAIN in the treatment of COVID-19, AKR1B1, IL1B, and GSK3B. It provides a new basis and provides great support for subsequent research on COVID-19.

The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients.

BACKGROUND: The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China. METHODS: The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package. RESULTS: A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis. CONCLUSIONS: For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.

The chemical mechanism of pestilences or Coronavirus disease 2019 (COVID-19)

Around the end of 2019, a new viral species caused large-scale transmissions and infections, discovered in Wuhan (WHO Emergencies Preparedness, Response, 2020) and subsequently around the world (WHO COVID-19 Disease Dashboard, 2020). Symptoms caused include coughing, shortness of breath, and fever. Around 1% to 5% (Worldometer, 2020) of confirmed infections have resulted in deaths, mainly due to severe respiratory failure (CDC, 2020). Genealogical tree studies of the new virus strains have later revealed them to be phylogenetically intimate relatives of the Severe Acute Respiratory Syndrome Coronavirus, namely (SARS-CoV), first identified in 2003 [1]. This new virus has been named SARS-CoV-2 by the International Committee on Taxonomy of Viruses (ICTV) (Gorbalenya et al., 2020) on February 11th, 2020.

Impact of COVID-19 on Urban Energy Consumption of Commercial Tourism City.

In 2020, the COVID-19 pandemic has spread worldwide. To alleviate this spread, various blockade policies have been implemented in many areas. This has led to a sluggish demand in the world's major economies, sharp drop in the trade index, and negative growth in energy consumption. To formulate a better epidemic prevention policy for urban energy consumption of commercial tourism cities, this study summarizes the major statistics of energy supply and demand before and during the epidemic period based on actual data. The characteristics of energy consumption in different sectors, including hotels, transportation, tourism culture, and public utilities, are then analyzed in detail. Finally, the energy consumption features of commercial tourism cities represented by Macao are compared to those of other typical countries (e.g., Italy, United States, Japan, and Brazil). These analyses demonstrate the impact of COVID-19 on the energy consumption in commercial tourism cities, which provides insights for the government or energy providers to formulate policies to adapt to this pandemic.

COVID-19 Epidemic Situation, the Outbreak Peak and Cycle Centered on China's No-Drug Control Actions: A Modeling Study (preprint)

Background: New characteristics have created significant challenges in non-drug regulation on the 2019 Corona Virus Disease (COVID-19) outbreak while there are few related research articles in recent months. In view of these recent developments, a more comprehensive analysis on the role of non-drug control measures (NDC) will provide a further guide for the work on disease prevention faced with a range of changes. Methods: This paper measures and divides the intensity of the NDC into eleven tiers according to the World Health Organization (WHO) and Chinese emergency classification criteria. The paper then proposes a new model for control-measures-susceptible-exposed-infected-removed-dead (CM-SEIRD), based upon the standard susceptible-exposed-infected-removed (SEIR) model, which parameterizing the new characteristics of COVID-19 infection and NDC. The start time and intensity of implementation of the NDC were taken as variables to obtain the special impact of NDC on the COVID-19 epidemic. The theoretical study on variable discretization and continuity was carried out for the Wuhan model system. Findings: NDC can efficiently minimize deaths, minimize infection risk, and delay the outbreak peak for 1-2 months. The highest outbreak time discussed in this study was around 16-23 days. If NDC can be carried out within the time threshold and intensity level, more than 10% of the total death toll will be reduced, more than 8% of the peak infected number will be reduced, and more than 90% of the population will be able to prevent infection. In contrast to the NDC starting time, the intensity of NDC impacts the outbreak more directly and has a good impact on the overall number of people exposed, sick and dead from the beginning to the end. This study also found that it would take 23 days to clear new cases in the blocked areas based on the strength of Chinese control measures. Interpretation: Despite great pressure due to the new characteristics and situation of the COVID-19 outbreak, at this point, NDC is still the most effective prevention and control step. Different countries and regions need to implement strict NDCs as quickly as possible to contain the outbreak and to promote development and economic growth as soon as possible.Funding Statement: Guangxi University.Declaration of Interests: All authors declare no competing interests.