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Objective: To explore the pathogen composition and distribution characteristics of pathogens in respiratory samples from patients with fever of unknown origin. Methods: A total of 96 respiratory samples of patients with unknown cause fever with respiratory symptoms were collected from four hospitals above grade II in Shijiazhuang area (Hebei Provincial Hospital of Traditional Chinese Medicine, Luancheng District People's Hospital, Luquan District People's Hospital, Shenze County Hospital) from January to April 2020, and multiplex-fluorescent polymerase chain reaction(PCR)was used to detect influenza A virus, influenza B virus, enterovirus, parainfluenza virus I/II/III/IV, respiratory adenovirus, human metapneumovirus, respiratory syncytial virus, human rhinovirus, human bocavirus, COVID-19, Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Pseudomonas aeruginosa, Streptococcus pneumoniae, Klebsiella pneumoniae, Group A streptococcus, Haemophilus influenzae, Staphylococcus aureus nucleic acid detection, the results were analyzed for chi-square. Results: A total of 8 pathogens were detected in the upper respiratory tract samples of 96 fever patients, including 1 kind of virus, 6 kinds of bacterias, and Mycoplasma pneumoniae. There were 12 viruses including influenza virus and parainfluenza virus, Legionella pneumophila and Chlamydia pneumoniae were not detected. The pathogen detection rates in descending order were Streptococcus pneumoniae (58/96, 60.42%), Haemophilus influenzae(38/96, 39.58%), Klebsiella pneumoniae (14/96, 14.58%), Staphylococcus aureus (10/96, 10.42%), Mycoplasma pneumoniae (8/96, 8.33%), Pseudomonas aeruginosa (6/96, 6.25%), Group A streptococcus (4/96, 4.17%) and human rhinovirus (2/96, 2.08%). The proportions of single-pathogen infection and multi-pathogen mixed infection in fever clinic patients were similar, 41.67% (40/96) and 45.83% (44/96), respectively, and 12.50% (12/96)of the cases had no pathogens detected. The infection rate of Mycoplasma pneumoniae in female patients with fever (21.43%) was higher than that in male patients with fever (2.94%) (P < 0.05). There was no statistical difference between the distribution of of other pathogens and gender and age(P > 0.05). Conclusions: The upper respiratory tract pathogens were mainly bacterial infections, and occasional human rhinovirus and Mycoplasma pneumonia infections. In clinical diagnosis and treatment, comprehensive consideration should be given to the pathogen detection.
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Objective: To investigate the relationship between negative attentional bias and post-traumatic stress disorder(PTSD) in the context of higher depression and anxiety symptoms after the outbreak of COVID-19, so as to provide scientific basis for mental health education in primary and secondary schools. Methods: From March to April 2021, a total of 708 students from primary school and junior high school (grade 6 through grade 9) in Beijing, Shanxi, Hunan, Shandong, Hebei, Hubei of China were selected. The Children's Revised Impact of Event Scale(CRIES), the Attention to Positive and Negative Information Scale (APNI)and Depression, Anxiety and Stress Scale-21(DASS-21) were used in a questionnaire survey. Results: A total of 242 students were diagnosed with PTSD, and the detection rate was 34.2%. The scores of intrusion and high arousal of boys(7.92+or-5.33, 8.60+or-5.41) were lower than those of girls(8.72+or-4.85, 9.50+or-4.76), and the difference was statistically significant (t=-2.04, -2.32, P < 0.05). There were statistically significant differences of negative attention bias, CRIES score, intrusion, debarb and high arousal among primary and middle school students of different grades (F=3.57, 5.99, 4.45, 4.60, 7.40, P < 0.05). Negative attention bias, anxiety, depression and post-traumatic stress symptoms were significantly positively correlated (r=0.27-0.84, P < 0.05). Logistic regression analysis showed that anxiety (OR=1.13, 95%CI=1.06-1.20) and negative attention bias (OR=1.10, 95%CI=1.07-1.12) were positively associated with PTSD symptoms in primary and middle school students(P < 0.01). Conclusion: Anxiety and depressive symptoms show impacts on negative attention bias and might exacerbate the symptoms of post-traumatic stress disorder. Therefore, emotional adjustments can help reduce the post-traumatic stress response in the post-epidemic period.
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Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clusters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall distribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and natural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and socio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental factors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution characteristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China.
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Purpose: Based on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic. Design/methodology/approach: A guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19. Findings: This research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed;perceived emotional values associated with peer-to-peer stays were more complex;perceived social values decreased, host-guest interactions were reduced and online communication became a stronger trend;tourist preferences for types of experiences changed, and people changed their destination selections;perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased. Research limitations/implications: This research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19. Originality/value: To the best of authors' knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.
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Purpose: COVID-19 affects the peer-to-peer (P2P) accommodation industry. With regard to prospect theory, individuals' negative emotions, such as institutional distrust, are easily evoked and impede consumption intention in an environment of permeating uncertainty and risks. While existing research indicates the negative effects of institutional distrust, scant research has explored its antecedents and intervention mechanisms. This study thus aims to unveil the influencing factors and explore mitigating mechanisms of customers' institutional distrust of P2P accommodations. Design/methodology/approach: Online reviews data were used to identify the underlying critical issues. The authors developed a model to depict how institutional distrust is formed under the boundary condition of subjective norm by the results. The model was verified using a questionnaire survey. Finally, in-depth semi-structured interviews were conducted to ensure its robustness. Findings: The external environment and internal platform effectiveness are two critical aspects affecting institutional distrust of P2P accommodations. The external environment influences institutional distrust through perceived threat, explaining the formation mechanism of customers' institutional distrust through customers' internal psychology. Furthermore, the authors found subjective norm moderating the effect of perceived threat on customers' institutional distrust. Research limitations/implications: This is one of the first studies, to the best of the authors' knowledge, to explore institutional distrust of P2P accommodations after COVID-19. The finding contributes to studies on P2P accommodation by uncovering the contingent role of subjective norm in influencing customers' institutional distrust. Originality/value: This is a pioneering study that explores the antecedents and mitigating mechanisms of institutional distrust of P2P accommodations during the new normal of COVID-19.
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Tourism is linked to multiple dimensions, such as the economy, society, and environment, and the relationships among its influencing factors are complex, diverse, and overlapping. This study constructed an evaluation index system to measure the degree of coordinated development of tourism, transportation, and the regional economy, then built a tourism-transportation-based Spatial Durbin Model (SDM) regarding the process of the coordinated development of tourism in the Beijing-Tianjin-Hebei region (BTHR) from 2010 to 2020. This paper explains the current status of sustainable tourism development in the BTHR and the impact and spillover effects of transportation on tourism development. The results show that the normalized tourism coordinated development index (NTCDI) of the BTHR increased from 13.61 in 2010 to 18.75 in 2019, then decreased to 14.45 in 2020. The results of SDM show that different transportation modes have different spillover effects on tourism. Specifically, civil aviation transportation has a positive impact and significant spillover on a city's tourism revenue (TR), while high-speed railway transportation has a negative spillover effect. The model results also show that the degree of openness of the city and city economic development level have significant positive effects and spillover effects on tourism development. Finally, the implications of related variables are discussed, and some suggestions are put forward on tourism development in the BTHR. However, there are some limitations in this study. In the future, international cooperation and data sharing will be strengthened, and multivariate methods such as social network analysis, artificial intelligence, and machine learning will be further integrated to achieve accurate simulation and prediction of the spatial spillover effects of tourism transportation.
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Since the outbreak of COVID-19, digital nomads, a group of people who work remotely while traveling, have been emerging as a hot research topic in the global academic community. With the rising number of digital nomads in China, several tourist destinations, such as Dali in Yunnan, and Anji in Zhejiang, have evolved into digital nomad destinations, and this trend is expanding. However, the concept of digital nomads remains relatively unexamined by China's academic community, as evidenced by the limited academic research and considerable lag of academic research behind industry practice. First, this paper establishes the conceptual foundations of digital nomads based on content analysis. It offers a detailed definition established through an exploration of the work, travel, and lifestyle perspectives of digital nomads. Six indicators have been identified to distinguish digital nomads from remote workers, business travelers, and working tourists, including location independence, mobility, work focus, work digitization, precarity, and travel frequency. Further, the characteristics (including the pursuit of freedom, uninterrupted travel, working while traveling, professional and informational literacy, and invasion and integration of the destination culture) and supporting systems (co-spaces, digital and technical support, and economic and social support) are discussed. Next, this paper investigated the origin and evolution of digital nomads through an analysis of the evolutionary paths of work style from nomadic worker to digital knowmad, and from knowledge worker to digital knowledge worker to digital knowmad, the evolution of leisure travel style from backpacker to flashpacker to flashpacker nomad and the evolution of lifestyle from nomad to global/neo-nomad to digital nomad. Finally, the current state of digital nomad research is surveyed, highlighting research themes, key theoretical frameworks, gaps in knowledge, and areas for future research. Insights into digital nomads are explored, including (1) the applicability of conceptual definitions of digital nomads at the operational level, (2) the practical validity of theoretical frameworks for digital nomad research, (3) the impact of digital nomads on the evolution of destinations, the urban-rural development, digital economic development, and the mechanisms involved, and (4) the emerging research on digital nomads in China with a focus on understanding the unique economic, social and cultural characteristics of digital nomads in China compared to other countries. Based on the insights shared in this paper, future research directions for digital nomads are envisaged. This paper establishes a baseline for the research on digital nomads to guide future scholarly attention toward digital nomad research and provide a basis for the development and practical application of relevant theories in the context of the digital economy.
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Background: Novel coronavirus pneumonia (NCP) and osteoporotic vertebral compression fractures (OVCF) are the high incidences of diseases in the elderly. During the epidemic period, if not treated in time, the complications are high and the mortality is high. If we do not pay attention to infection prevention and control in pre-hospital emergency care, it will lead to the first time infection of medical staff and in-hospital cross infection in emergency outpatient receiving area. The correct consideration of both and the establishment of perfect pre-hospital emergency treatment and infection prevention and control synchronous strategy is an important premise to ensure the stable, orderly and safe medical treatment. Background: To explore the effect of synchronous implementation of pre-hospital emergency care, nursing and infection pre-vention and control for senile OVCF during the epidemic. In order to improve the efficiency of pre-hospital emergency care and prevent the spread of infection. Method: A total of 92 elderly patients with OVCF who received pre-hospital treatment in 18 hospitals in Zhangjiakou City during the epidemic prevention and control period from January 2020 to November 2022 and met the inclusion criteria were selected as research objects, including 24 males and 68 females, aged 65 - 82 (74.2 +or- 2.2) years. All patients were associated with concomitant injuries and underlying diseases. All patients in this group underwent predictive pre-hospital rescue and infection prevention and control procedures. Results: All the 92 elderly patients with OVCF received timely pre-hospital treatment during the epidemic period, and no aggravation occurred of the 92 patients, 35 were in the high risk area, 10 were in the medium risk area, and 47 were in the low risk area. Exclude OVCF for NCP Patients were treated according to the conventional diagnosis and treatment principles. Suspected and confirmed cases are transferred to designated surgical hospitals for treatment. All patients were followed up 3 months, 6 months and 12 months after treatment. There was no death rate, high satisfaction of pre-hospital first aid, high diagnostic accuracy, and good curative effect. None of the rescue personnel had any infection rate, and no hospital infection transmission and nosocomial cross infection occurred. Conclusion: It is the first step to safely treat patients and prevent cross infection to establish a perfect synchronous strategy of pre-hospital first aid and infection prevention and control.
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The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.
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To investigate the characteristics of particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) and its chemical compositions in the Beijing-Tianjin-Hebei (BTH) region of China during the novel coronavirus disease (COVID-19) lockdown, the ground-based data of PM2.5, trace gases, water-soluble inorganic ions, and organic and elemental carbon were analyzed in three typical cities (Beijing, Tianjin, and Baoding) in the BTH region of China from 5-15 February 2020. The PM2.5 source apportionment was established by combining the weather research and forecasting model and comprehensive air quality model with extensions (WRF-CAMx). The results showed that the maximum daily PM2.5 concentration reached the heavy pollution level (>150 µg/m3) in the above three cities. The sum concentration of SO42-, NO3- and NH4+ played a dominant position in PM2.5 chemical compositions of Beijing, Tianjin, and Baoding; secondary transformation of gaseous pollutants contributed significantly to PM2.5 generation, and the secondary transformation was enhanced as the increased PM2.5 concentrations. The results of WRF-CAMx showed obviously inter-transport of PM2.5 in the BTH region; the contribution of transportation source decreased significantly than previous reports in Beijing, Tianjin, and Baoding during the COVID-19 lockdown; but the contribution of industrial and residential emission sources increased significantly with the increase of PM2.5 concentration, and industry emission sources contributed the most to PM2.5 concentrations. Therefore, control policies should be devoted to reducing industrial emissions and regional joint control strategies to mitigate haze pollution.
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Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Beijing , COVID-19/epidemiology , China/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Particulate Matter/analysisABSTRACT
Traditional buses travel on fixed routes and areas, which cannot satisfy the flexible demands of athletes in the context of COVID-19 and the closed-loop traffic management policy during the 2022 Beijing Winter Olympic Games (BWOG). This study predicts the travel demands based on the characteristics of athletes' daily travel demands and then presents a flexible bus service scheduling model for cross-region scheduling among Beijing, Yanqing, and Zhangjiakou to provide high-level service. The flexible bus service is point-to-point and avoids unnecessary contact, which reduces the risk of spreading COVID-19 and ensures athletes' safety. In this study, the flexible bus scheduling model is established to optimize scheduling schemes, whose object is to minimize the cost of the system based on some realistic constraints. These constraints consider not only the preferred time windows of athletes' demand but also the vehicle's capacity, depot, minimum load factor, total demands, etc. In addition, a genetic-simulated annealing hybrid algorithm (GSAHA) is designed to solve the model based on the characteristics of the genetic algorithm (GA) and simulated annealing. To assess the feasibility and efficiency of the model and algorithm, a case study is conducted in the Beijing-Yanqing area. Furthermore, the travel time of the flexible bus is compared to that of the traditional bus, according to the results of the case study. Moreover, the sensitivity of the model and algorithm are analyzed. The experimental results show that the proposed model and algorithm can dispatch buses with superior flexibility and high-level services during the BWOG. [ FROM AUTHOR]
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A series of lockdown measures in response to the Corona Virus Disease 2019 (COVID-19) outbreak resulted in a drop in anthropogenic emissions and changes in concentrations of PM2.5 and O-3. Backward trajectories analysis, cluster analysis, potential source contribution function (PSCF) and concentration weighted trajectory (CWT) technologies were conducted to reveal the characteristics and potential source areas of pollutants in Beijing before the COVID-19 outbreak (BCO period), during the outbreak (COB period) and after the outbreak (ACO period), as well as the contemporaneous period in 2019 (CCO period), which is critical for exploring the efficient control measures and making policy. The results indicated that despite the significant reduction in anthropogenic emissions during the epidemic, the PM2.5 concentrations increased by 1.0% caused by unfavorable meteorological conditions. O-3 concentrations increased by 174.8% compared to that during the BCO period due to the increased temperature and inappropriate precursor reduction ratios. A considerable decrease of NO3- in PM2.5 was observed under the influence of significant reductions in vehicle emissions during the lockdown. The cluster analysis revealed that short-range transport played a significant role in the accumulation of local PM2.5 pollution, while long-range northwest airflows contributed more to O-3 accumulation, and weakened as the season changed. The PSCF and CWT analysis demonstrated that potential source areas of PM2.5 were mostly located in the central and southern Hebei, the southwestern Shandong in the CCO period, and expanded to central Inner Mongolia and northern Shanxi in the COB period. These areas were highly compatible with the high emission areas of the emission inventory statistics. After the outbreak, the source areas of O-3 were centered in the Beijing-Tianjin-Hebei region and Shandong province, with a radial dispersion in all directions, while they were distributed in the central Mongolia and Inner Mongolia during the other periods.
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Studies have shown that during the 2020 epidemic control period, air quality data has been"abnormal", but due to differences in geography and demographic factors, the survey results in different regions are not consistent. The purpose of the study is to analyze the difference in air quality under the control of the two epidemics in Shijiazhuang City, further explore the impact of control measures on air quality, and provide reference and ideas for improving air quality and adjusting atmospheric control measures. Based on the China Air Quality Online Monitoring and Analysis Platform and Zhenqi.com, with Shijiazhuang air quality as the research object, the air quality and meteorological monitoring data in the past 6 years have been collected and sorted, and the t-test and rank-sum test methods are used to analyze the air pollution status and change characteristics in the two epidemic control periods based on the air quality monitoring data of Shijiazhuang City during the epidemic control period from January 5 to February 8,2021, with comparison with the data of the same time period in 2019. With reference to changes in meteorological factors, the impact of epidemic control on air quality was comprehensively analyzed. Results indicate that in 2021, the social blockade measures against the epidemic have greatly reduced the concentrations of various pollutants. The concentrations of PM2.5,PM10,SO2,CO2, and NO2 have dropped by 52.13%,40.62%,74.93%,55.53%,44.59%,(p<0.05),respectively, compared with the same period before the epidemic (2016-2019 average levels).O3 pollution increased, and the concentration increased by 49.40%compared to before the epidemic (p<0.05);the severity of epidemic control measures also has a certain impact on air quality compared with the epidemic blockade carried out in the same period in 2020.PM2.5,PM10,CO2,and NO2 during the epidemic control period in 2021 decreased by 54.85%,37.79%,52.36%and 31.15%,respectively,(p<0.05). Analysis of meteorological factors showed that there was no significant difference in the average temperature and wind speed in Shijiazhuang during the survey period from January 5 to February 8 in the past 6 years. Compared with before the epidemic, there was no significant change in the relative humidity during the epidemic period in 2021. The relative humidity in the same period in 2020 was 65.28% higher than before the epidemic (p<0.05). There were obvious differences in air quality during the two epidemic control periods in Shijiazhuang, but the meteorological factors did not show corresponding changes. The change in air quality may be related to the strictness of the epidemic control measures and the order of implementation.
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Objectives: To investigate the risk factors for a blood infection in patients with lymphoma in order to guide the clinical prevention and control Of infection. Method: Clinical data were collected from 434 patients with lymphoma in 2019. including 231 males and 203 females. Blood samples were collected for culture, and the pathogens were identified by a VITEK-32 automated microbial identification system. PCR was used to detect Virulence genes of Klebsiella pneumoniae. Data were collected, such as patients' age, disease status, clinical stage, and pathological type. and risk factors affecting the incidence of infection in patients were analyzed using a Chi-square test.
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Objective: To analyze the epidemiological characteristics and spatial-temporal clustering of coronavirus disease 2019(COVID-19)in Hebei Province in order to provide scientific basis for the formulation of prevention and control measures.
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To better understand the change characteristics and reduction in organic carbon (OC) and elemental carbon (EC) in particulate matter (PM) with a diameter of ≤2.5 µm (PM2.5) driven by the most stringent clean air policies and pandemic-related lockdown measures in China, a comprehensive field campaign was performed to measure the carbonaceous components in PM2.5 on an hourly basis via harmonized analytical methods in the Beijing-Tianjin-Hebei and its surrounding region (including 2 + 26 cities) from January 1 to December 31, 2020. The results indicated that the annual average concentrations of OC and EC reached as low as 6.6 ± 5.7 and 1.8 ± 1.9 µg/m3, respectively, lower than those obtained in previous studies, which could be attributed to the effectiveness of the Clean Air Action Plan and the impact of the COVID-19-related lockdown measures implemented in China. Marked seasonal and diurnal variations in OC and EC were observed in the 2 + 26 cities. Significant correlations (p < 0.001) between OC and EC were found. The annual average secondary OC levels level ranged from 1.8-5.4 µg/m3, accounting for 37.7-73.0% of the OC concentration in the 2 + 26 cities estimated with the minimum R squared method. Based on Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithms, the light extinction contribution of carbonaceous PM to the total amount reached 21.1% and 26.0% on average, suggesting that carbonaceous PM played a less important role in visibility impairment than did the other chemical components in PM2.5. This study is expected to provide an important real-time dataset and in-depth analysis of the significant reduction in OC and EC in PM2.5 driven by both the Clean Air Action Plan and COVID-19-related lockdown policies over the past few years, which could represent an insightful comparative case study for other developing countries/regions facing similar carbonaceous PM pollution.
Subject(s)
Air Pollutants , COVID-19 , Aerosols/analysis , Air Pollutants/analysis , COVID-19/prevention & control , Carbon/analysis , China , Cities , Communicable Disease Control , Environmental Monitoring , Humans , Particle Size , Particulate Matter/analysis , SeasonsABSTRACT
Properly addressing external shocks in urban agglomeration is critical to sustaining the complex regional system. The COVID-19 pandemic has been widely acknowledged as an unintended external shock, but the temporal and spatial transmission patterns are largely ignored. This study analyzed the temporal and spatial transmission patterns of COVID-19 at the macro, meso, and micro levels, and proposes a conceptual model for regional comprehensive risk calculation, taking the Beijing-Tianjin-Hebei (BTH) area as the focus region. Our results showed that 1) at the temporal scale, the epidemic in the BTH area experienced stages of rapid increase, gradual decrease, and stabilization, and the first wave of the epidemic was under control from 23 February 2020; 2) at the spatial scale, confirmed cases were largely distributed at the terminal of the migration network, with closely interconnected cities in the BTH area, including Beijing, Tianjin, Tangshan, and Langfang, holding the highest comprehensive epidemic risk, thus requiring special attention for epidemic prevention and control. Finally, a "two-wheels" conceptual framework was built to discuss implications for future policies for addressing external shocks. Our proposed framework consists of an isolation wheel, which involves information sharing from the holistic perspective, and a circulation wheel, which emphasizes stakeholder involvement from the individual perspective. The findings of this study provide a knowledge basis for epidemic prevention and control as well as useful implications for addressing external shocks in the future.
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COVID-19 , Beijing/epidemiology , COVID-19/epidemiology , Cities , Humans , PandemicsABSTRACT
Objective: To model an outbreak of coronavirus disease 2019 (COVID-19) in Shijiazhuang and forecast its spread trend. Method: We collected confirmed COVID-19 cases from the Health Commission of Hebei Province during the period of January 2 to January 27, 2021. We built a new model (SEIaIcRK), including the contribution of asymptomatic cases, based on the traditional SEIR model to explore and analyze the transmission of COVID-19.
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As an important symbol and carrier of regional social and economic activities, population mobility is a vital force to promote the re-agglomeration and diffusion of social and economic factors. An accurate and timely grasp on the impact of the COVID-19 epidemic on population mobility between cities is of great significance for promoting epidemic prevention and control and economic and social development. This study proposes a theoretical framework for resilience assessment, using centrality and nodality, hierarchy and matching, cluster, transmission, and diversity to measure the impact of the COVID-19 epidemic on population mobility in the Beijing-Tianjin-Hebei (BTH) urban agglomeration in 2020-2022, based on the migration data of AutoNavi and social network analysis. The results show that the COVID-19 epidemic had different impacts on the population network resilience of the BTH urban agglomeration based on the scale and timing. During the full-scale outbreak of the epidemic, strict epidemic prevention and control measures were introduced. The measures, such as social distancing and city and road closure, significantly reduced population mobility in the BTH urban agglomeration, and population mobility between cities decreased sharply. The population mobility network's cluster, transmission, and diversity decreased significantly, severely testing the network resilience. Due to the refinement of the epidemic control measures over time, when a single urban node was impacted, the urban node did not completely fail, and consequently it had little impact on the overall cluster, transmission, and diversity of the population mobility network. Urban nodes at different levels of the population mobility network were not equally affected by the COVID-19 epidemic. The findings can make references for the coordination of epidemic control measures and urban development. It also provides a new perspective for the study of network resilience, and provides scientific data support and a theoretical basis for improving the resilience of BTH urban agglomeration and promoting collaborative development.
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During the lockdown implemented to curb the spread of COVID-19, human activities have drastically reduced, providing a valuable opportunity to study and compare the impact of meteorological conditions and human activities on air quality. In this study, large-scale weather circulation, local meteorological conditions, and the impact of human activities are comprehensively considered, and changes in the concentration of major air pollutants in the northeast during this period are systematically studied. The large-scale weather circulation patterns that mainly affect the northeast region are divided into nine types by using the T-mode Principal components analysis objective circulation classification method. It is found that the northeast region is located at the edge of weak high pressure (Types 1, 2, and 7) and at the rear of high pressure (Type 4) and has higher concentrations of PM2.5, NO2, SO2, and CO;in cyclonic weather systems, low vortices (Types 3 and 5) and under the influence of the updraft (Type 6) in front of the trough, the ozone concentration is higher. The changes in the concentrations of PM2.5, NO2, CO, SO2, and O-3 in the three cities, namely Shenyang, Changchun, and Harbin, during the lockdown period are compared, and it is found that the concentrations of PM2.5, NO2, CO, and SO2 have a tendency to first decrease and then increase, while the changes of O-3 concentration are cyclical and increased significantly during this period. This demonstrates that pollutants such as PM2.5, NO2, CO, and SO2 are more susceptible to human activities and local meteorological conditions, and changes in O-3 concentration are more closely related to changes in weather circulation types. Finally, the FLEXPART-WRF model is used to simulate the pollution process of nine circulation types, which confirms that particulate pollution in the northeast is mainly affected by local emissions and local westward sinking airflow.