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This paper examines the debate over the place of "companion animal" public space in China's cities. With the COVID-19 outbreak, this debate has entered a new phase, where the social response to the outbreak may have fundamentally changed the public's use and perception of "companion animal" public and pseudo-public space. This paper combines quantitative and qualitative analysis of posts and comments on two of China's largest social media platforms with a big data approach, based on a case study in Beijing, China. There were statistically significant differences in the perception and use of "companion animal" public spaces and pseudo-public spaces before and after the pandemic. We attribute the impact of the pandemic on "companion animal" spaces to three pathways: changes in opportunity, changes in ability, and changes in motivation. We found that the pandemic led to an increase in the amount of time available to some people but a decrease in the amount of "companion animal" public space available due to the pandemic closure. In addition, the use of "companion animal" public spaces in pseudo-public spaces declined, while those located within the open urban green space on the city's outskirts stood out after the outbreak. With the normalisation of the pandemic, there will be new challenges for the development and operation of companion-animal-related public spaces in cities, which will be the next focus of research. In addition, governments and social media should work together to promote and support sustainable animal ethical practices to better respond to the crisis. These findings will help complement the urban services system and guide future planning, design, and evaluation of related spaces. [ABSTRACT FROM AUTHOR]

COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users' movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China's policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing. [ABSTRACT FROM AUTHOR]

COVID-19 affects aviation around the world. China's civil aviation almost recovered to its pre-epidemic levels in the domestic market, but there are still local outbreaks that affect air traffic. This paper proposes measuring the impact of local outbreaks of COVID-19 by the machine learning method and the synthetic control method as a counterfactual control group to measure such an impact. In this study, we use the LightGBM algorithm to construct a counterfactual control group and transform the prediction problem from time series to the fitting problem at the spatial level. We find that machine learning methods can measure such an impact more accurately. We take local outbreaks in Beijing and Dalian as examples, and our measure of their impacts shows that the impact of an outbreak on intercity air traffic can be divided into lag, decline, stable, and recovery periods, and will last for a long period (more than 40 days) unless there are external stimuli, such as legal holidays. The outbreaks reduced the number of passengers in the cities by 90%. Finally, we show the impact on the air traffic network, and find that when a local outbreak happens in a big city, tourist cities or small stations will be greatly affected. [ABSTRACT FROM AUTHOR]

COVID-19 leads small and medium-sized enterprises (SMEs) to survive very hard. The development difficulties of SMEs lead to weak employment and GDP growth in various countries. In the process of COVID-19's continuous spread, what is the major reason for the difficulties of SMEs? This paper hopes to answer this question by studying SMEs in Beijing. On this basis, this paper uses structural equation model (SEM) to study the relatively fast recovery of SMEs in Beijing, China, to explore the factors affecting SMEs in the pandemic. After detailed desk research and interviews with relevant entrepreneurs, this paper collects 234 valid questionnaires from SMEs in various industries in Beijing with the help of Federation of Industry and Commerce and Chamber of Commerce in Beijing. Then the data is analyzed with the SEM, which shows the relationship between cash flow from financing activities, markets, employees, costs, government policies and the impact of the pandemic. Finally, an impact model of the pandemic on SMEs is established. The result of the model indicates that the direct effect of the pandemic on the market is the most prominent, and government policies can significantly reduce the negative impact of the pandemic on SMEs indirectly. Based on this, this paper puts forward some policy suggestions, such as the targeted issuance of consumption vouchers and the reduction of administrative barriers. This will enable megacities in various countries to improve policy support for SMEs and promote the recovery and development of SMEs. [ABSTRACT FROM AUTHOR]

Background: A novel coronavirus (COVID-19) outbreak has occurred in China, and national medical workers have been thrown into this silent battle. Paediatric medical workers have been an important part of this battle and under enormous pressure. This paper evaluates the depression, anxiety, and stress of paediatric medical staff during the epidemic and examines related impact factors. Methods: We conducted this study using online questionnaires via social networking software during the week of Feb. 17 to Feb. 23, 2020. The 21-item Depression Anxiety Stress Scale (DASS), which is a revised, simplified version of the original DASS developed by Lovibond et al., was used in this study. Results: Among all 2,031 respondents, 14.81%, 18.3%, and 9.98% had depression, anxiety and stress symptoms, respectively. Males, doctors, individuals aged between 31‑60 years, those with senior job titles, those who had contact with patients with confirmed or suspected cases of COVID-19, those who worked on the clinical frontlines fighting the epidemic and those who had experience combating similar outbreaks were more likely to have depression, anxiety or stress symptoms. Respondents in Beijing and Chongqing had lower negative psychological symptom scores than the national average. Conclusion: During the COVID-19 outbreak, depression, anxiety and stress are present to varying degrees among paediatric medical workers across the country. Psychological crisis interventions should be implemented to protect the mental health of paediatric medical workers during and after the epidemic. [ABSTRACT FROM AUTHOR]

With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm. [ABSTRACT FROM AUTHOR]

During the global COVID-19 crisis, China has portrayed itself as a "champion of multilateralism" -- defending UN institutions and its own multilateral initiatives in a variety of spheres. China's approach has, however, often been criticised for undermining multilateralism through its use of multilateral platforms as arenas to contest US leadership. This paper examines the official Chinese discourse on multilateralism during the COVID 19 crisis in the years 2020 and 2021 in light of Beijing's multilateral diplomacy in the Global South, exemplified by the Forum on China-Africa Cooperation (FOCAC) and the China-CELAC Forum (China--Community of Latin American and Caribbean States Forum). The authors argue that China's growing multilateral engagement must be understood in the context of a multilateral system that was already in crisis before the pandemic due to its inability to resolve major global challenges. The term "multilateralism" is essentially used by Beijing to discursively oppose "unilateralism". While its active multilateral engagement may be able to prevent and resolve major global crises in the future, Beijing's approach offers limited multilateral spaces for deliberation and contestation by weaker states and non-state actors. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to investigate the changes of epidemic characteristics of influenza activity pre– and post–coronavirus disease 2019 (COVID-19) in Beijing, China. Methods Epidemiologic data were collected from the influenza surveillance system in Beijing. We compared epidemic intensity, epidemic onset and duration, and influenza transmissibility during the 2022–2023 season with pre-COVID-19 seasons from 2014 to 2020. Results The overall incidence rate of influenza in the 2022–2023 season was significantly higher than that of the pre-COVID-19 period, with the record-high level of epidemic intensity in Beijing. The onset and duration of the influenza epidemic period in 2022–2023 season was notably later and shorter than that of the 2014–2020 seasons. Maximum daily instantaneous reproduction number (Rt) of the 2022–2023 season (Rt = 2.31) was much higher than that of the pre-COVID-19 period (Rt = 1.49). The incidence of influenza A(H1N1) and A(H3N2) were the highest among children aged 0–4 years and 5–14 years, respectively, in the 2022–2023 season. Conclusions A late, intense, and short-term peak influenza activity was observed in the 2022–2023 season in Beijing. Children <15 years old were impacted the most by the interruption of influenza circulation during the COVID-19 pandemic. Maintaining continuous surveillance and developing targeted public health strategies of influenza is necessary. [ABSTRACT FROM AUTHOR]

Background: The Chinese government has ended the "dynamic zero-COVID" policy, and residents are now living together with the SARS-CoV-2 virus. Only a limited number of studies have investigated the specific content and structure of COVID-19-related risk perceptions, as well as their underlying determinants. This study measured the residents' risk perception of COVID-19 and analyzed the predictors of RP. Methods: We conducted a comprehensive questionnaire-based survey among residents mostly in Beijing, using a specially designed scale consisting of 11 items to accurately measure COVID-19 risk perceptions. We then utilized multiple linear regression analysis to investigate the factors associated with risk perceptions. Results: A total of 60,039 residents participated in the survey. Our study reveals that COVID-19-related worries are significantly influenced by other dimensions of RP (p < 0.001), except for perceived society's control of the epidemic. Several experiential and socio-demographic factors, including gender, educational level, and infectious experience, are notably correlated with all dimensions of risk perceptions of COVID-19. Conclusion: This study evaluates the specific content and structure of COVID19-related risk perceptions, as well as their determinants. It is essential to understand the risk perceptions and health-protective behaviors of residents with diverse educational levels, incomes, and medical histories. [ABSTRACT FROM AUTHOR]

The mass media serves as an indispensable force in the prevention and control of epidemics, and plays a vital role in information distribution, public opinion guidance and confidence enhancement during a pandemic of infectious diseases. However, due to the particularity of infectious disease outbreaks, interview work in medical institutions should be conducted in an orderly and efficient manner under the guidance of hospitalacquired infection control and prevention protocols. This paper summarizes measures for infection control during news material collection in isolation wards from Beijing’s medical aid team to Hubei province in the fight against COVID-19, so as to provide a reference for the media to carry out relevant work on the prevention and control of infectious diseases in the future. [ABSTRACT FROM AUTHOR]

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare acquired disease characterized by chronic complement-mediated hemolysis. The concentrated outbreak of coronavirus disease 2019 (COVID-19) in China after 6 December 2022, provided an opportunity to observe the disease course of PNH during an active Omicron infection epidemic. Patients diagnosed with PNH at Peking Union Medical College Hospital (PUMCH) before 6 December 2022, were followed up until 10 April 2023. Clinical data related to coronavirus infection and hemolysis were recorded. Factors influencing the infection and severity rate of Omicron, as well as hemolysis provocation, were analyzed. In total, 131 patients with PNH were included in this retrospective analysis; 87.8% were infected with Omicron. Among them, 15.7% met the criteria for severity, and 1 patient died (0.87%). No protective factors were identified against Omicron infections. However, patients with severe Omicron infection (n = 18) had a lower vaccination rate than those with non-severe infection (n = 97; p = 0.015). Among those infected (n = 115) with Omicron, there was a significant increase in lactate dehydrogenase (LDH) levels compared with those in the uninfected group (n = 16, p = 0.000). Patients with severe infections (n = 18) had even higher LDH increase rates than those without severe infections (n = 97; p = 0.002). 10 (37.0%) patients treated with complement inhibitors developed breakthrough hemolysis (BTH). Patients treated with complement inhibitors (n = 27) exhibited less severe hemolysis than treatment-naïve patients (n = 104; p = 0.003). Omicron infection exacerbates hemolytic attacks in patients with PNH. Vaccination helps mitigate the severity of Omicron infection, and using complement inhibitors reduces hemolysis exacerbation. [ABSTRACT FROM AUTHOR]

Background: An international public health emergency has resulted from the emergence of the new coronavirus-2. Both direct and indirect influences have been felt on the treatment of acute stroke. However, no conclusive link between COVID-19 and the alleged decline in admissions for stroke-related reasons has been established, and the findings of recent studies are contentious. Most of those researches are not made use of authentic data. Therefore, the goal of this study is to examine how the COVID-19 pandemic has affected hospital admissions for stroke patients, to provide a basis for managing stroke patients in hospitals during COVID-19 pandemic.Methods: This retrospective study took place at the People's Hospital of Peking University in Beijing, China. For each patient, interventions such as the application of tissue plasminogen activator (tPA) were examined. We recorded each patient's outcomes. The stroke patients' hospital admissions were compared to the average of the prior year. As the time span of interest, we selected the epidemic period (January 2020 to December 2020).Results: When compared to the previous year in the pre-COVID period, mean monthly stroke hospitalizations decreased during the pandemic by 54.42% (P< 0.001); average lengths of stay (ALOS) increased; and non-emergency admissions decreased by 78.8% (P< 0.001). The monthly volume of stroke patients exceeding the intravenous thrombolysis (IVT) window decreased by 25% and 59.73%, respectively (P < 0.001). There was a 5.3% increase in the percentage of IVT or within IVT time window in male stroke patients (p=0.019; p=0.049). During COVID-19, the proportion of non-local patients among male stroke patients decreased by 10% compared with the previous year (p=0.006).Conclusion: The COVID-19 epidemic has had a negative impact on stroke management in China, healthcare systems must work to assess and adapt to the new reality. [ABSTRACT FROM AUTHOR]

Background: The world in recent years has seen a pandemic of global scale. To counter the widespread loss of life and severe repercussions, researchers developed vaccinations at a fast pace to immunize the population. While the vaccines were developed and tested through extensive human trials, historically vaccines have been known to evoke mixed sentiments among the generic demographics. In the proposed study, we aim to reveal the impact of political and socio-economic factors on SARS-Cov-2 vaccination trends observed in two hundred and seventeen countries spread across the six continents. Methods: The study had hypothesized that the citizens who have lower trust in their government would be less inclined towards vaccination programs. To test this hypothesis, vaccination trends of nations under authoritarian rule were compared against democratic nations. Further, the study was synthesized with Cov-2 vaccination data which was sourced from Our World Data repository, which was sampled among 217 countries spread across the 6 continents. The study was analyzed with exploratory data analysis and proposed with relevance and impacting factor that was considered for vaccine dissemination in comparison with the literacy rate of the nations. Another impacting factor the study focused on for the vaccination dissemination trends was the health expenses of different nations. The study has been synthesized on political and socio-economic factors where the features were ardently study in retrospect of varied socio- economic features which may include country wise literacy rate, overall GDP rate, further we substantiated the work to address the political factors which are discussed as the country status of democratic or having other status. Results: The comparison of trends showed that dissemination of SARS-Cov-2 vaccines had been comparable between the two-opposing types of governance. The major impact factor behind the wide acceptance of the SARS-Cov-2 vaccine was the expenditure done by a country on healthcare. These nations used a large number of vaccines to administer to their population and the trends showed positive growth. The overall percentage of vaccine utilized by countries in quantitative terms are Pfizer/BioNTech (17.55%), Sputnik V (7.08%), Sinovac (6.98%), Sinopharm/Beijing (10.04%), Oxford/AstraZeneca (19.56%), CanSino (2.85%), Moderna (12.05%), Covaxin (3.28%), JohnsonandJohnson (10.89%), Sputnik Light (3.07%), Novavax (3.49%). While the nations with the lowest healthcare expenses failed to keep up with the demand and depended on vaccines donated by other countries to protect their population. Conclusions: The analysis revealed strong indicators that the nations which spend more on healthcare were the ones that had the best SARS-Cov-2 vaccination rollout. To further support decision-making in the future, countries should address the trust and sentiment of their citizens towards vaccination. For this, expenses need to be made to develop and promote vaccines and project them as positive health tools. [ABSTRACT FROM AUTHOR]

BACKGROUND: To assess the efficacy of the epidemic prevention measures of the "closed-loop" system adopted by the Beijing 2022 Olympic Winter Games (BOWG). METHODS: We retrospectively collected and analyzed information, including age, sex, nationality, vaccination status, date of diagnosis, and date of entry, from 280 SARS-CoV-2-positive individuals identified during the BOWG. A susceptibility-exposed-infectious-remove model was employed to evaluate the effectiveness of epidemic prevention strategies on controlling the spread of SARS-CoV-2 under different scenarios during the BOWG. RESULTS: Regarding SARS-CoV-2-positive cases, 97.9% were imported, and 96.4% were asymptomatic. The median age was 37 years (range: 29-47 years), and 73.9% were male, with the majority of cases being broadcasters and European attendees. Regarding vaccination status, 93.5% were fully vaccinated, and six cases were considered to have been infected in the closed-loop system during the BOWG. Assuming that the BOWG adopted a semi-closed-loop management system, the cumulative number of confirmed cases would be 1,137 for quick quarantine measures (3 d later) implemented and 5,530 for delayed quarantine measures (9 d later) implemented. This modeling revealed that stringent pandemic prevention measures and closed-loop management effectively controlled the spread of SARS-CoV-2 during the BOWG. CONCLUSION: Imported cases are considered the main risk factor for SARS-CoV-2 transmission during mass gatherings, but a comprehensive closed-loop system could minimize transmission among attendees and general personnel. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 outbreak has hit Beijing since mid-Nov, 2022, with soaring growth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among children. Therefore, it is vital to determine the clinical manifestations of epidemic SARS-CoV-2 strains in paediatric patients. Methods: In this study, nucleic acid tests (NATs) for SARS-CoV-2 were performed in paediatric outpatients with symptoms of acute respiratory tract infection during 18 Nov–6 Dec, 2022. Half of the outpatients positive for SARS-CoV-2 were randomly selected to screen for other respiratory pathogens, whereas those with low cycle threshold values in SARS-CoV-2 NATs were amplified and sequenced to determine the SARS-CoV-2 variants. Finally, children positive for SARS-CoV-2 with clinical information in detail were enrolled in a follow-up study to identify potential factors significantly associated with long recovery. Results: Among 9625 paediatric outpatients tested for nucleic acid of SARS-CoV-2, 733 (7.62%, 733/9625) were identified as SARS-CoV-2 NAT positive, with only three (0.82%, 3/366) co-infected with other pathogens among 366 randomly selected patients, and 71 (62.83%) determined as Omicron subvariant BF.7 and 42 (37.22%) as BA.5.2 among 113 successfully sequenced. Among the 681 patients with complete clinical information, fever was the most common symptom (96.8%). In a follow-up study of 592 patients, 46.96% became asymptomatic on the third day and 65.71% on the fifth day. Only 1.7% of infected children experienced febrile seizures. Combined with abnormal C-reactive protein, a higher percentage of antibiotics administration was observed. More co-living members and longer duration of first symptoms served as independent risk factors for long-term recovery, especially in children vaccinated for SARS-CoV-2. Conclusions: BF.7 and BA.5.2 were the dominate Omicron subvariants and caused milder infections during the SARS-CoV-2 outbreak in Beijing. The number of co-living members and duration of first symptoms were independent risk factors for long-term recovery. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm–bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures. [ABSTRACT FROM AUTHOR]

Background: Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time. Methods: The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing. Results: The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium–high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually. Conclusions: The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases. [ABSTRACT FROM AUTHOR]

Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction's historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment. [ABSTRACT FROM AUTHOR]

Pekar et al. (2022) propose that SARS-CoV-2 was a zoonotic spillover that first infected humans in the Huanan Seafood Market in Wuhan, China. They propose that there were two separate spillovers of the closely related lineages A and lineage B in a short period of time. The two lineages are differentiated by two SNVs; hence, a single-SNV A-B intermediate must have occurred in an unsampled animal host if the two-spillover hypothesis is correct. Consequently, confirmation of the existence of an intermediate A-B genome from humans would falsify their hypothesis of two spillovers. Pekar et al. identified and excluded 20 A-B intermediate genomes from their analysis. A variety of exclusion criteria were applied, including low read depth and the assertion of repeated erroneous base calls at lineage-defining positions 8782 and 28144. However, data from GISAID show that most of the genomes were sequenced to high average sequencing depth, appearing inconsistent with these criteria. The decision to exclude the majority of genomes was based on personal communications, with raw data unavailable for inspection. Multiple errors, biases, and inconsistencies were observed in the exclusion process. For example, 12 intermediate genomes from one study were excluded; however, 54 other genomes from the same study were included, indicating selection bias. Puzzlingly, two intermediate genomes from Beijing were discarded despite an average sequencing depth of 2175X; however, four genomes from the same sequencing study were included in the analysis. Lastly, we discuss 14 additional possible intermediate genomes not discussed by Pekar et al. and note that genome sequence filtration is inappropriate when considering the presence or absence of a specific SNV pair in an outbreak. Consequently, we find that the exclusion of many of the intermediate genomes is unfounded, leaving the conclusion of two natural zoonoses unsupported. [ABSTRACT FROM AUTHOR]

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing's geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing's transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. [ABSTRACT FROM AUTHOR]

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NO x concentration; instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF. [ABSTRACT FROM AUTHOR]

Background: The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. Methods: During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. Results: At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. Conclusions: Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results. [ABSTRACT FROM AUTHOR]

Currently, an inactivated vaccine has been widely used with encouraging results as a prophylactic agent against COVID-19 infection, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. However, in vitro SARS-CoV-2 vaccine-specific immune features remain elusive, hindering the promotion of a third dose of the vaccine. Here, we present a detailed in vitro immune cellular response and large-scale multi-omics analysis for peripheral blood mononuclear cells (PBMCs) from participants vaccinated with CoronaVac (Sinovac Life Sciences, Beijing, China) and recovered participants from COVID-19. The mean titers of SARS-CoV-2 serum-neutralizing antibodies were significantly increased after the boosting immunization (Day 45) compared to the unimmunized state. We observed that type-1 helper T cells (Th1) tended to dominate after the first dose of vaccine, while humoral immune responses became dominant after the second dose due to the activation of type-2 helper T cell (Th2), memory B cells, and plasmablasts. T follicular helper cells (Tfh) involved in antibody production were activated after the first dose and were maintained for the observed time points. Single-cell RNA sequencing of PBMCs revealed specific changes in cell compositions and gene expression in immunized participants. Multi-omics analysis also demonstrated that CoronaVac-specific serum proteins, plasma metabolites, and plasma lipid changes were skewed to those changes in convalescent patients. Collectively, we provide a comprehensive understanding of CoronaVac-specific in vitro immune features. [ABSTRACT FROM AUTHOR]

Purpose: To understand the relationship between sense of place and creativity and the mechanisms that affect this relationship, the researchers constructed a mediation model to examine the effect of sense of place on creativity and the mediating role of psychological resilience in elementary school students during the COVID-19 pandemic. Methods: A total of 1,711 students in an elementary school in Beijing, China, were surveyed using Chinese-language versions of the Sense of Place Scale, the Psychological Resilience Scale-Short Version, and the Innovative Behavior Inventory. SPSS (version 23) and PROCESS plug-in (version 3.3) were used for correlation and mediation analyses. Results: (1) Correlation analysis revealed that sense of place was positively related to psychological resilience (r = 0.445, p < 0.01) and creativity (r = 0.590, p < 0.01). (2) Psychological resilience was also positively correlated with creativity (r = 0.625, p < 0.01). (3) Further, after controlling for gender and grade level, it was found that sense of place directly predicted creativity and that sense of place also indirectly predicted creativity through psychological resilience. The direct effect (0.45) and the mediating effect (0.23) accounted for 65.95 and 34.05% of the total effect, respectively. Conclusion: The results demonstrate that sense of place is a positive predictor of creativity and can play a facilitating role to some extent. Moreover, psychological resilience is a mediating factor, acting as a buffer between sense of place and creativity. These results contribute to a more comprehensive understanding of the mechanisms influencing creativity. [ABSTRACT FROM AUTHOR]

Since 2019, the coronavirus disease 2019 (COVID-19) global pandemic has caused more than 300 million cases of disease and 5 million deaths. Vaccination has been widely accepted as the most effective measure for the prevention and control of this disease. However, there is little understanding about serum anti-SARS-CoV-2 IgM/IgG levels after inactivated vaccination as well as the relationship with peripheral blood leukocytes in the non-COVID-19 infected population. A total of 16,335 male and 22,302 female participants were recruited in this study, which was conducted in the Peking University Third Hospital located in Beijing (China). The level and seroprevalence of serum anti-SARS-CoV-2 receptor-binding domain (RBD) IgM/IgG and the association with peripheral blood leukocytes classification were investigated. With an increase in the number and percentage of full immunization of COVID-19 vaccinations in Beijing, serum anti-SARS-CoV-2 IgG antibodies levels and seroprevalence were significantly elevated (p < 0.01). The serum anti-SARS-CoV-2 IgG antibodies of 60 years and older persons were significantly lower than that of individuals that are 18~60 years old (p < 0.01), and there was a positive relationship between serum anti-SARS-CoV-2 IgG antibodies levels and peripheral blood lymphocyte count. The investigation of serum anti-SARS-CoV-2 IgM/IgG antibodies and the peripheral hematological index may prompt and help understand the adaptive immune response of vaccination. [ABSTRACT FROM AUTHOR]

Background: Nucleic acid test (NAT) could effectively control the spread of COVID-19 caused by large-scale sports competitions. However, quantitative analysis on the appropriate frequency of NAT is scarce, and the cost-effectiveness and necessity of high-frequency NAT remain to be fully explored and validated. This study aims to optimize the COVID-19 surveillance strategies through cost-effectiveness analysis for the Tokyo 2020 Olympic Games and the upcoming Beijing 2022 Olympic Winter Games. Methods: A total of 18 scenarios were designed regarding the NAT frequency, symptom monitoring, and strengthening close-contact control. An agent-based stochastic dynamic model was used to compare the cost-effectiveness of different NAT scenarios and optimize the surveillance strategies. The dynamics of the proposed model included the arrival and departure of agents, transmission of the disease according to Poisson processes, and quarantine of agents based on regular NATs and symptom onset. Accumulative infections, cost, and incremental cost-effectiveness ratio (ICER) were simulated in the frame of the model. ICER was used to compare the cost-effectiveness of different scenarios. Univariate sensitivity analysis was performed to test the robustness of the results. Results: In Scenario 16, where the competition-related personnel (CRP) received NAT daily and national sports delegation (NSD) with quarantined infections accepted an additional NAT daily, accumulative infection was 320.90 (90 initial infections), the total cost was (United States Dollar) USD 8 920 000, and the cost of detecting out each infection was USD 27 800. Scenario 16 would reduce the total cost by USD 22 570 000 (avoid 569.61 infections), USD 1 420 000 (avoid 47.2 infections) compared with Scenario 10 (weekly NAT, strengthened close contact control) and Scenario 7 (daily NAT, no strengthened close contact control), respectively. Sensitivity analysis showed that the result was most sensitive to the change in basic reproductive number. Conclusions: High-frequency NATs such as bidaily, daily, and twice a day were cost-effective. NAT daily for CRP with strengthening close-contact control could be prioritized in defense against COVID-19 at large-scale sports competitions. This study could assist policymakers by assessing the cost-effectiveness of NAT scenarios and provide the host country with an optimal COVID-19 surveillance strategy. [ABSTRACT FROM AUTHOR]

Background: The COVID-2019 pandemic has placed extensive pressure on health systems and posed a severe public health challenge worldwide. Lockdown measures implemented in many countries have delayed virus spread. However, a considerable number of people have faced unprecedented pressure, especially pregnant and breast-feeding women, because face-to-face professional support has been reduced during the lockdown in many countries.Objectives: To compare the delivery and infant feeding experiences of women who delivered before (BL) versus during (DL) the Covid-19 pandemic in Beijing, China and to investigate predictors of breastfeeding at 6-months.Methods: Women aged ≥18 years with an infant ≤18 months of age completed an anonymous survey. Information/links were shared online and via local clinics in Beijing. Logistic regression was performed to assess predictors of breastfeeding during the first 6-months.Results: One thousand eight hundred seven women provided data; BL 1231 (68.1%), DL 576 (31.9%). Significantly more mothers in DL group reported the lockdown had moderate to high impact to their household income (p = 0.013) and the convenience of purchasing daily necessities(p = 0.014). Compared to BL mothers, significantly more mothers in the DL groups thought their birth location and breastfeeding intention had been effected by the COVID-19 (p < 0.001, p = 0.036 respectively). Mostly breastfeeding (MBF, mainly breastfeeding with few non-formula fluids added) at 6 months was predicted by delivery during the lockdown period (OR1.43, 95% confidence interval (CI) 1.08, 1.90), younger maternal age (OR 0.96, 95%CI 0.93, 0.99), getting support from friends or relatives (OR 1.95, 95%CI 1.06, 3.59), and discussing health issues in online groups > four times a week (OR 1.66, 95%CI 1.09, 2.53).Conclusion: The COVID-19 pandemic and lockdown measures influenced mothers' planned birth location and breastfeeding intention. However, breastfeeding practice was maintained during the pandemic. Our results highlight the importance of feeding support as well as potential beneficial effects of increased mother-infant contact during the lockdown period which is relevant even under normal circumstances. [ABSTRACT FROM AUTHOR]

The Chinese Communist Party is consolidating one party rule under the leadership of Xi Jinping. Beijing seeks to rule by central mandate while limiting local autonomy. The central government response to the COVID‐19 public health emergency reinforces this view. In January 2020 Beijing established the Central Epidemic Response Leading Group to mobilise a comprehensive nationwide policy effort to contain the virus. The exceptional nature of the COVID‐19 national emergency allows the central government to project power over local authorities and leverage over citizens, but we argue that this is a short‐term phenomenon because local disease control initiatives remain important, with local authorities adapting national policies to meet constituent needs. There are degrees of policy discretion and divergence at the subnational level that enable context‐specific responses to the virus within China's strict bureaucratic hierarchy. Primary data derives from interviews and observations in Nancun village, Hebei Province, conducted from January to April 2020. Evidence from Nancun explains how local authorities interpret the edicts and mandates of the central government. [ABSTRACT FROM AUTHOR]

Objective: To analyze the discrepancy between self-rating and professional evaluation of mental health status in coronavirus disease 2019 (COVID-19) cluster cases. Method: A total of 65 COVID-19 cluster cases admitted to Beijing Ditan Hospital Capital Medical University from June 14, 2020 to June 16, 2020 were included in the study. Mental health assessment was completed by self-rating and professional evaluation. The gaps between self-rating and professional evaluation in different demographic characteristics were compared. Results: The results of self-rating were inconsistent with those of professional evaluation. The gap was statistically different among certain demographic subgroups. As for anxiety, the gaps had remarkable statistics differences in subgroups of sex, monthly income, infection way, and anxiety/depression medical history. Similarly, in the terms of depression, the gaps had significant statistic differences in the subgroups of the medical history of anxiety/depression, history of physical disease, employment status and the insurance type, marriage, education (year), residing in Beijing (year), and the monthly income. Conclusion: Compared to the professional evaluation, patients had a higher self-rating, which may be related to some demographic characteristics. It suggests that screening can be conducted in patients with COVID-19 by self-rating first, and then professional evaluation should be carried out in the patients with suspicious or positive results. [ABSTRACT FROM AUTHOR]

The frequent occurrence of local COVID‐19 today gives a strong necessity to better understand the effects of "source‐case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID‐19 cases, which originated in the Xinfadi market in Beijing from June–July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero‐inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID‐19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID‐19 was identified in both global and local modeling estimation. The dominant role of the "source‐case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID‐19. Plain Language Summary: The frequent occurrence of local COVID‐19 is becoming a global public health problem today. Most previous studies have focused on the influence of the "density" of socio‐economic and demographic factors on the spatial clustering of COVID‐19 (named "density‐driven" in this study) while ignoring the effect of the spatial distance between the source of the local COVID‐19 and the cases (named "distance‐driven" in this studies). Therefore, this manuscript took the local COVID‐19 cases which originated from the Xinfadi market from June–July 2020 as the study case. To obtain more accurate results, two improved quantitative research methods are adopted. It was found that the roles which the "source‐case" distance played were far more decisive than the scholars thought before. We also found that the local epidemic had a significant trend of changing from the "distance‐driven" diffusion mode to the "density‐driven" diffusion mode, and the critical point between these two driving modes will be one of the keys to prevent and control the clustering. Key Points: The spatiotemporal characteristics of localized spatial clustering of COVID‐19 were quantitively analyzed by multimethodThe improved quantitative research methods were applied in the modeling of the spatial clustering factors at the micro scaleThe trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted [ABSTRACT FROM AUTHOR]

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. • We build a system for detecting COVID-19 infection regions. • We build a dataset of COVID-19. • It is easy to distinguish pneumonia from healthy cases. [ABSTRACT FROM AUTHOR]

To prevent the spread of the COVID-19 epidemic, restrictions such as "lockdowns" were conducted globally, which led to a significant reduction in fossil fuel emissions, especially in urban areas. However, CO 2 concentrations in urban areas are affected by many factors, such as weather, biological sinks and background CO 2 fluctuations. Thus, it is difficult to directly observe the CO 2 reductions from sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the restrictions, to determine the reduction signals. We conducted six series of on-road CO 2 observations in Beijing using mobile platforms before (BC), during (DC) and after (AC) the implementation of COVID-19 restrictions. To reduce the impacts of weather conditions and background fluctuations, we analyze vehicle trips with the most similar weather conditions possible and calculated the enhancement metric, which is the difference between the on-road CO 2 concentration and the "urban background" CO 2 concentration measured at the tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. The results showed that the DC CO 2 enhancement was decreased by 41 (±1.3) parts per million (ppm) and 26 (±6.2) ppm compared to those for the BC and AC trips, respectively. Detailed analysis showed that, during COVID-19 restrictions, there was no difference between weekdays and weekends during working hours (09:00–17:00 local standard time; LST). The enhancements during rush hours (07:00–09:00 and 17:00–20:00 LST) were almost twice those during working hours, indicating that emissions during rush hours were much higher. For DC and BC, the enhancement reductions during rush hours were much larger than those during working hours. Our findings showed a clear CO 2 concentration decrease during COVID-19 restrictions, which is consistent with the CO 2 emissions reductions due to the pandemic. The enhancement method used in this study is an effective method to reduce the impacts of weather and background fluctuations. Low-cost sensors, which are inexpensive and convenient, could play an important role in further on-road and other urban observations. [ABSTRACT FROM AUTHOR]

Background: With the spread of SARS-CoV-2 worldwide, understanding the basic epidemiological parameter values of COVID-19 from real-world data in mega-cities is essential for disease prevention and control.Methods: To investigate the epidemiological parameters in SARS-CoV-2 infected cases in Beijing, we studied all confirmed cases and close contacts in Beijing from Jan 1st to Apr 3rd 2020. The epidemiological and virological characteristics of SARS-CoV-2 were analyzed.Results: A total of 602 cases were positive for SARS-CoV-2, including 585 confirmed patients and 17 asymptomatic infections. The imported cases were mainly from Wuhan initially and then from abroad. Among 585 confirmed case-patients, the median age was 39 years old. The mean incubation period was 6.3 days. The secondary attack rate among households was higher than social contacts (15.6 vs 4.6%). The secondary attack rate of healthcare workers (HCWs) was higher than non-HCWs' (7.3 vs 4.2%). The basic reproduction number was 2.0, and the average serial interval was 7.6 days. No significant genetic variant was identified.Conclusions: The transmissibility of SARS-CoV-2 was relatively high, especially among households and from HCWs, which draws specific public health attention. So far, no evidence of widespread circulation of SARS-CoV-2 in communities in Beijing was found. [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus Disease (COVID-19) in Wuhan have affected more than 250 countries and regions worldwide. However, most of the clinical studies have been focused on Wuhan, and little is known about the disease outside of Wuhan in China. In this retrospective cohort study, we report the early clinical features of 80 patients with COVID-19 admitted to the hospital in Beijing. The results show that 27 (33.8%) patients had severe illness. Six (7.5%) patients were admitted to the ICU, and 3 (3.8%) patients died. Forty-eight percent (39/80) of the patients had a history of living/traveling in Wuhan. Patients with severe- illness were significantly older (average age, 71 years old vs 44 years old) and had a high incidence of expectoration (59.3% vs 34.0%), shortness of breath (92.6% vs 9.4%), anorexia (51.9% vs 18.9%) and confusion(18.5% vs 0%) compared with nonsevere patients. The systolic blood pressure (median, 130 mmHg vs 120 mmHg) was higher and the oxygen saturation (median, 98.3% vs 92.0%) was significantly lower in severe patients than nonsevere patients. In addition, myoglobin (median, 56.0 ng/mL vs 35.0 ng/mL), troponin I (median, 0.02 pg/mL vs 0.01 pg/mL), C-reactive protein (median, 69.7 mg/L vs 12.9 mg/L) and neutrophils (median, 3.3×109/L vs 2.2×109/L) were significantly increased, while lymphocytes (median, 0.8×109/L vs 1.2×109/L), albumin (mean, 32.8 g/L vs 36.8 g/L) and the creatinine clearance rate (median, 91.2 vs 108.2 ml/min/1.73m2) were significantly decreased among severe patients. Our study revealed that older patients with high levels of C-reactive protein, myoglobin, troponin I, and neutrophil and high systolic blood pressure as well as low levels of lymphocytes, and albumin and a low creatinine clearance rate and oxygen saturation were more likely to have severe disease. [ABSTRACT FROM AUTHOR]

The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing's non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing's industry, the deterioration of its air quality would also bring pressure to Beijing's atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a major challenge for society as a whole, and analyzing the impact of the spread of the epidemic and government control measures on the travel patterns of urban residents can provide powerful help for city managers to designate top-level epidemic prevention policies and specific epidemic prevention measures. This study investigates whether it is more appropriate to use groups of POIs with similar pedestrian flow patterns as the unit of study rather than functional categories of POIs. In this study, we analyzed the hour-by-hour pedestrian flow data of key locations in Beijing before, during, and after the strict epidemic prevention and control period, and we found that the pedestrian flow patterns differed greatly in different periods by using a composite clustering index; we interpreted the clustering results from two perspectives: groups of pedestrian flow patterns and functional categories. The results show that depending on the specific stage of epidemic prevention and control, the number of unique pedestrian flow patterns decreased from four before the epidemic to two during the strict control stage and then increased to six during the initial resumption of work. The restrictions on movement are correlated with most of the visitations, and the release of restrictions led to an increase in the variety of unique pedestrian flow patterns compared to that in the pre-restriction period, even though the overall number of visitations decreased, indicating that social restrictions led to differences in the flow patterns of POIs and increased social distance. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to ongoing reductions in economic activity and anthropogenic emissions. Beijing was particular badly affected by lockdown measures during the early months of the COVID-19 pandemic. It has significantly reduced the CO2 emission and toxic air pollution (CO and NO2). We use column-averaged dry-air mole fractions of CO2 and CO (XCO2 and XCO) observed by a ground-based EM27/SUN Fourier transform spectrometer (FTS), the tropospheric NO2 column observed by MAX-DOAS and satellite remote sensing data (GOSAT and TROPOMI) to investigate the variations in anthropogenic CO2 emission related to COVID-19 lockdown in Beijing. The anomalies describe the spatio-temporal enhancement of gas concentration, which relates to the emission. Anomalies in XCO2 and XCO, and XNO2 (ΔXCO2, ΔXCO, and ΔXNO2) for ground-based measurements were calculated from the diurnal variability. Highly correlated daily XCO and XCO2 anomalies derived from FTS time series data provide the ΔXCO to ΔXCO2 ratio (the correlation slope). The ΔXCO to ΔXCO2 ratio in Beijing was lower in 2020 (8.2 ppb/ppm) than in 2019 (9.6 ppb/ppm). The ΔXCO to ΔXCO2 ratio originating from a polluted area was significantly lower in 2020. The reduction in anthropogenic CO2 emission was estimated to be 14.2% using FTS data. A comparable value reflecting the slowdown in growth of atmospheric CO2 over the same time period was estimated to be 15% in Beijing from the XCO2 anomaly from GOSAT, which was derived from the difference between the target area and the background area. The XCO anomaly from TROPOMI is reduced by 8.7% in 2020 compared with 2019, which is much smaller than the reduction in surface air pollution data (17%). Ground-based NO2 observation provides a 21.6% decline in NO2. The NO2 to CO2 correlation indicates a 38.2% decline in the CO2 traffic emission sector. Overall, the reduction in anthropogenic CO2 emission relating to COVID-19 lockdown in Beijing can be detected by the Bruker EM27/SUN Fourier transform spectrometer (FTS) and MAX-DOAS in urban Beijing. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) is pandemic. It is critical to identify COVID-19 patients who are most likely to develop a severe disease. This study was designed to determine the clinical and epidemiological features of COVID-19 patients associated with the development of pneumonia and factors associated with disease progression. Methods: Seventy consecutive patients with etiologically confirmed COVID-19 admitted to PLA General Hospital in Beijing, China from December 27, 2019 to March 12, 2020 were enrolled in this study and followed-up to March 16, 2020. Differences in clinical and laboratory findings between COVID-19 patients with pneumonia and those without were determined by the χ2 test or the Fisher exact test (categorical variables) and independent group t test or Mann–Whitney U test (continuous variables). The Cox proportional hazard model and Generalized Estimating Equations were applied to evaluate factors that predicted the progression of COVID-19. Results: The mean incubation was 8.67 (95% confidence interval, 6.78–10.56) days. Mean duration from the first test severe acute respiratory syndrome coronavirus 2-positive to conversion was 11.38 (9.86–12.90) days. Compared to pneumonia-free patients, pneumonia patients were 16.5 years older and had higher frequencies of having hypertension, fever, and cough and higher circulating levels of neutrophil proportion, interleukin-6, low count (< 190/µl) of CD8+ T cells, and neutrophil/lymphocyte ratio. Thirteen patients deteriorated during hospitalization. Cox regression analysis indicated that older age and higher serum levels of interleukin-6, C-reactive protein, procalcitonin, and lactate at admission significantly predicted the progression of COVID-19. During hospitalization, circulating counts of T lymphocytes, CD4+ T cells, and CD8+ T cells were lower, whereas neutrophil proportion, neutrophil/lymphocyte ratio, and the circulating levels of interleukin-6, C-reactive protein, and procalcitonin were higher, in pneumonia patients than in pneumonia-free patients. CD8+ lymphocyte count in pneumonia patients did not recover when discharged. Conclusions: Older age and higher levels of C-reactive protein, procalcitionin, interleukin-6, and lactate might predict COVID-19 progression. T lymphocyte, especially CD8+ cell-mediated immunity is critical in recovery of COVID-19. This study may help in predicting disease progression and designing immunotherapy for COVID-19. [ABSTRACT FROM AUTHOR]