Heat waves accelerate the spread of infectious diseases.

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

Deep learning-based network pharmacology for exploring the mechanism of licorice for the treatment of COVID-19.

Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach to identify more potential active compounds and targets of licorice. 4 compounds (quercetin, naringenin, liquiritigenin, and licoisoflavanone), 2 targets (SYK and JAK2) and the relevant pathways (P53, cAMP, and NF-kB) were predicted, which were confirmed by previous studies to be associated with SARS-CoV-2-infection. In addition, 2 new active compounds (glabrone and vestitol) and 2 new targets (PTEN and MAP3K8) were further validated by molecular docking and molecular dynamics simulations (simultaneous molecular dynamics), as well as the results showed that these active compounds bound well to COVID-19 related targets, including the main protease (Mpro), the spike protein (S-protein) and the angiotensin-converting enzyme 2 (ACE2). Overall, in this study, glabrone and vestitol from licorice were found to inhibit viral replication by inhibiting the activation of Mpro, S-protein and ACE2; related compounds in licorice may reduce the inflammatory response and inhibit apoptosis by acting on PTEN and MAP3K8. Therefore, licorice has been proposed as an effective candidate for the treatment of COVID-19 through PTEN, MAP3K8, Mpro, S-protein and ACE2.

Empirical Study on Design Trend of Taiwan (1960s–2020): The Evolution of Theme, Diversity and Sustainability

With significance in improving and developing local design culture as well as in supplementing global design history, this essay describes a study on the past and a clear prediction of the future by exploring Taiwan’s design history from approximately the 1960s to 2020 based on the evolution of theme, diversity, and sustainability. In this research, the Python programming language is used to apply three algorithms of term frequency–inverse document frequency (TF-IDF), Simpson’s diversity index (SDI), and latent Dirichlet allocation (LDA) to conduct a text exploration of design journals. The results show the following: in the 1960s–1980s, the evolution of theme focused on evaluation strategies, technical practices, and foreign cultures, on digital design, multiculturalism, and design aesthetics in the 1990s, and on emotional human factors, intelligent technology, and local culture since the beginning of the 21st century. Local culture and intelligent technology are the main driving forces of the current design industry. Regarding diversity, after a period of rapid change and stable rising, it has shown a downward trend in recent years. This indicates that current design needs to be stimulated by external environmental variations. Sustainability was focused on technology, the market, and education during the 1960s–1980s;on consumers, design education, and eco-design during the 1990s;and on integration across fields during the 2000s–2020. In order to gain a wider perspective of the complete design context of Chinese culture, the results show the current and future trends of the academic community, in addition to a reference for the study of the design histories of other areas in the world.

CSES Satellite Observations of 50 Hz Power Line Radiation Over Mainland China and Its Response to COVID‐19

We present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19.Alternate :Plain Language SummaryPower line harmonic radiation (PLHR) is the electromagnetic waves radiated by electric power systems at harmonic frequencies of 50 or 60 Hz, depending on the frequency of the system on the ground. Previous research mainly focuses on identification of individual PLHR events and their subsequent analysis. However, the number of base‐frequency PLHR signal events is the most abundant, which is suitable for the statistical study of PLHR occurrence rate and its variation pattern, and further study of the factors affecting its variation pattern. In this paper, we use 3 years of electric field data from the China Seismo‐Electromagnetic Satellite (CSES) which is an LEO satellite launched into orbit in February 2018 to investigate the month‐to‐month variation pattern of PLHR occurrence rate over Mainland China and its correlation with the influencing factors. The response of PLHR occurrence rate to COVID‐19 are also investigated.

Water Transmission Increases the Intensity of COVID-19 Outbreaks.

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding.

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

Water Transmission Increases the Intensity of COVID-19 Outbreaks

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

The impact of crowd gatherings on the spread of COVID-19.

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown.

Estimating the impacts on PM2.5 pollution and CO2 emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO2 emissions from the Carbon Monitor project. Our study shows that PM2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM2.5 concentration. Furthermore, CO2 concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO2 emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO2 emissions.

Possible link between new coronavirus variants and atmospheric lightning and seawater intrusion

SARS-CoV-2 (COVID-19) has been affecting the world for more than one year. The appearance of the new coronavirus variants makes the current situation full of uncertainty. In this respect, we discuss the connection between virus mutation and atmospheric factors. Based on the process of nitrogen fixation and transformation of nitrate inside the human body, we propose that the new coronavirus variants might be related to lightning and seawater intrusion. Our study provides a new perspective in terms of the possible mechanism underlying the emergence of new coronavirus variants.

The global transmission of new coronavirus variants.

Coronavirus Disease 2019 (COVID-19) has caused tremendous losses to the world. This study addresses the impact and diffusion of the five major new coronavirus variants namely Alpha, Beta, Gamma, Eta, and Delta lineage. The results of this study indicate that Africa and Europe will be affected by new coronavirus variants the most compared with other continents. The comparative analysis indicates that vaccination can contain the spread of the virus in most of the continent, and non-pharmaceutical interventions (NPIs), such as restriction on gatherings and close public transport, will effectively curb the pandemic, especially in densely populated continents. According to our Global Prediction System of COVID-19 Pandemic, the diffusion of delta lineage in the US shows seasonal oscillation characteristics, and the first wave will occur in October 2021, with the record of 323,360, and followed by a small resurgence in April 2022, with the record of 184,196, while the second wave will reach to 232,622 cases in October 2022. Our study will raise the awareness of new coronavirus variants among the public, and will help the governments make appropriate directives to cope with the new coronavirus variants.

The oscillation-outbreaks characteristic of the COVID-19 pandemic.

The evolution of the COVID-19 pandemic features the alternation of oscillations and abrupt rises. The oscillations are attributable to weekly and seasonal modulations, while abrupt rises are stimulated by mass gatherings.

Correlation Analysis Between Serum Uric Acid, Prealbumin Level, Lactate Dehydrogenase, and Severity of COVID-19.

Objective: To analyze the correlation between serum uric acid, prealbumin levels, lactate dehydrogenase (LDH), and the severity of COVID-19. Methods: The data from 135 patients with COVID-19 was collected, and the patients were divided into a non-severe group (110 cases) and a severe group (25 cases), according to the severity of illness. Sixty cases with normal physical examinations over the same period and 17 cases diagnosed with other viral pneumonia in the past five years were selected as the control group to analyze the correlation between the detection index and the severity of COVID-19. Results: Serum albumin and prealbumin in the severe group were significantly lower than those in the non-severe group (p < 0.01); serum uric acid in the severe group was lower than that in the non-severe group (p < 0.05). LDH and C-reaction protein (CRP) in the severe group were higher than those in non-severe group (p < 0.01); the levels of albumin, prealbumin, serum uric acid, and LDH in the severe group were significantly different from those in healthy control group (p < 0.01) and the levels of prealbumin, serum uric acid, LDH, and CRP in the severe group were significantly different from those in the other viral pneumonia group (p < 0.01). Serum albumin and prealbumin were positively correlated with the oxygenation index (p < 0.001), while LDH was negatively correlated with oxygenation index (p < 0.001). Conclusion: Serum albumin, prealbumin, the oxygenation index, and LDH are risk factors of COVID-19.

Gamma-Glutamyltransferase Elevation Is Frequent in Patients With COVID-19: A Clinical Epidemiologic Study.

A newly identified coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the infectious coronavirus disease 2019 (COVID-19), emerged in December 2019 in Wuhan, Hubei Province, China, and now poses a major threat to global public health. Previous studies have observed highly variable alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in patients with COVID-19. However, circulating levels of the cholangiocyte injury biomarker gamma-glutamyltransferase (GGT) have yet to be reported in the existing COVID-19 case studies. Herein, we describe the relationship between GGT levels and clinical and biochemical characteristics of patients with COVID-19. Our study is a retrospective case series of 98 consecutive hospitalized patients with confirmed COVID-19 at Wenzhou Central Hospital in Wenzhou, China, from January 17 to February 5, 2020. Clinical data were collected using a standardized case report form. Diagnosis of COVID-19 was assessed by symptomatology, reverse-transcription polymerase chain reaction (RT-PCR), and computed tomography scan. The medical records of patients were analyzed by the research team. Of the 98 patients evaluated, elevated GGT levels were observed in 32.7%; increased C-reactive protein (CRP) and elevated ALT and AST levels were observed in 22.5%, 13.3%, and 20.4%, respectively; and elevated alkaline phosphatase (ALP) and triglycerides (TGs) were found in 2% and 21.4%, respectively. Initially, in the 82 patients without chronic liver disease and alcohol history, age older than 40 years (P = 0.027); male sex (P = 0.0145); elevated CRP (P = 0.0366), ALT (P < 0.0001), and ALP (P = 0.0003); and increased TGs (P = 0.0002) were found to be associated with elevated GGT levels. Elevated GGT (P = 0.0086) and CRP (P = 0.0162) levels had a longer length of hospital stay. Conclusion: A sizable number of patients with COVID-19 infection have elevated serum GGT levels. This elevation supports involvement of the liver in persons with COVID-19.

Clinical features and viral RNA shedding of imported and local cases with COVID-19 in Wenzhou, China.

ABSTRACT: Wenzhou had the highest number of confirmed novel coronavirus 2019 (COVID-19) cases outside the Hubei province. The aim of this study was to identify the difference in clinical features and viral RNA shedding between the imported and local COVID-19 cases in Wenzhou.All patients with confirmed COVID-19 admitted to Wenzhou Sixth People's Hospital, Wenzhou Central Hospital Medical Group, from January 17 to February 11, 2020, were enrolled in this study. Data was analyzed and compared for the imported and local cases with regard to epidemiological, demographic, clinical, radiological features, and laboratory findings. Outcomes for the enrolled participants were followed up until May 7, 2020.Of the 136 cases, 50 were imported from Wuhan. The median age was 45 years and 73 (53.7%) were men. The most common symptoms at onset were fever (104 [76.5%]) and cough (85[62.5%]). Pleural effusion was more common among imported cases compared to local cases. The white blood cell count, neutrophil count, lymphocyte count and platelet count of the imported cases were significantly lower than those of the local cases, while the prothrombin time was significantly longer than that of the local cases. Severe and critically ill patients accounted for 15.4% and 2.9%, respectively. The median duration of SARS-CoV-2 RNA shedding from symptom onset was 26 days (IQR 17-32.3 days) and there were no significant differences in duration of viral RNA shedding between the two groups.The study findings suggest that imported cases from Wuhan were more likely to be severe compared to the local cases in Wenzhou. However, there was no difference between imported and local cases on the viral shedding among the COVID patients.

The role of seasonality in the spread of COVID-19 pandemic.

It has been reported that the transmission of COVID-19 can be influenced by the variation of environmental factors due to the seasonal cycle. However, its underlying mechanism in the current and onward transmission pattern remains unclear owing to the limited data and difficulties in separating the impacts of social distancing. Understanding the role of seasonality in the spread of the COVID-19 pandemic is imperative in formulating public health interventions. Here, the seasonal signals of the COVID-19 time series are extracted using the EEMD method, and a modified Susceptible, Exposed, Infectious, Recovered (SEIR) model incorporated with seasonal factors is introduced to quantify its impact on the current COVID-19 pandemic. Seasonal signals decomposed via the EEMD method indicate that infectivity and mortality of SARS-CoV-2 are both higher in colder climates. The quantitative simulation shows that the cold season in the Southern Hemisphere countries caused a 59.71 ± 8.72% increase of the total infections, while the warm season in the Northern Hemisphere countries contributed to a 46.38 ± 29.10% reduction. COVID-19 seasonality is more pronounced at higher latitudes, where larger seasonal amplitudes of environmental indicators are observed. Seasonality alone is not sufficient to curb the virus transmission to an extent that intervention measures are no longer needed, but health care capacity should be scaled up in preparation for new surges in COVID-19 cases in the upcoming cold season. Our study highlights the necessity of considering seasonal factors when formulating intervention strategies.

Environmental Indicator for COVID-19 Non-Pharmaceutical Interventions.

A novel coronavirus (COVID-19) has caused viral pneumonia worldwide, posing a major threat to international health. Our study reports that city lockdown is an effective way to reduce the number of new cases and the nitrogen dioxide (NO2) concentration can be used as an environmental lockdown indicator to evaluate the effectiveness of lockdown measures. The airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas since the lockdown. The total number of newly confirmed cases reached an inflection point about two weeks since the lockdown and could be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly, and at the same time, the reduction rate of NO2 concentration will increase. Our research results show that NO2 satellite observations can help decision makers effectively monitor and manage non-pharmaceutical interventions in the epidemic.

Optimal parameterization of COVID-19 epidemic models

ABSTRACT At the time of writing, coronavirus disease 2019 (COVID-19) is seriously threatening human lives and health throughout the world. Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization. To capture and understand the characteristics of the epidemic trend, parameter optimization algorithms are needed to obtain model parameters. In this study, the authors propose using the Levenberg–Marquardt algorithm (LMA) to identify epidemic models. This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters. The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method. The results show that the Statistical-SIR (Statistical-Susceptible–Infected–Recovered) model using LMA can fit the actual curve of the epidemic well, while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation. 摘要 现如今, 新冠肺炎(COVID-19)严重威胁着世界各国人民的生命健康.许多流行病学模型已经被用于为政策制定者和世界卫生组织提供决策参考.为了更加深刻的理解疫情趋势的变化特征, 许多参数优化算法被用于反演模型参数.本文提议使用结合了高斯-牛顿法和梯度下降法的Levenberg–Marquardt(LMA)算法来优化模型参数.使用四个病例数相对较多的国家来验证这一算法的优势:相较于传统流行病学模型模拟曲线过早过快的到达峰值, 应用LMA的Statistical-SIR(Statistical-Susceptible–Infected–Recovered)模型可以更好地拟合实际疫情曲线.