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Abstract Background Hepatitis C virus (HCV) stands at the forefront of global elimination endeavors by 2030, highlighting the need for a nuanced exploration into disparities and vulnerabilities using innovative spatial epidemiological approaches. This study aimed to systematically review existing literature to summarize area-level factors associated with HCV infection and to examine the application of spatial and spatiotemporal analyses in HCV research. Methods A systematic search following PRISMA guidelines was conducted for peer-reviewed literature published between 2000 and 2023 using PubMed, Web of Science, Scopus, and Embase databases. The synthesis of area-level factors was organized according to four distinct categories of risk environments: social, economic, policy, and physical environments. Results Sixty-five studies were selected for this systematic review. 60% of the studies focused on the general population, while 20% of the studies targeted people who inject drugs. The area-level factors explored predominantly were characteristics of the social and economic risk environments. For instance, areas with a higher level of socioeconomic disadvantage, lower education attainment, higher population density, and located more remotely were associated with higher HCV infection rates. Additionally, some studies noted a significant correlation between the accessibility to harm reduction and healthcare services and HCV occurrence, testing, and treatment rates. Furthermore, spatial data exploration and cluster analysis methods were the predominant methods used to explore the nuanced spatial distribution of HCV infection. Conclusion This review emphasizes the imperative of deciphering the complex interplay of area-level factors in HCV infection dynamics. Understanding the potential risk environment landscape of HCV could facilitate identifying vulnerable areas and communities. Additionally, the limited application of spatial analytics in HCV research highlights the untapped potential, emphasizing the need for enhanced spatial techniques to pinpoint priority areas for intervention.

Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise.

This systematic review explores the utilization of crowdsourcing for geoinformation in enhancing awareness and mitigating terrorism-related disasters. Out of 519 studies identified in the database search, 108 were deemed eligible for analysis. We focused on articles employing various forms of crowdsourcing platforms, such as Twitter (now known as X), Facebook, and Telegram, across three distinct phases of terrorism-related disasters: monitoring and detection, onset, and post-incident analysis. Notably, we placed particular emphasis on the integration of Machine Learning (ML) algorithms in studying crowdsourced terrorism geoinformation to assess the current state of research and propose future directions. The findings revealed that Twitter emerged as the predominant crowdsourcing platform for terrorism-related information. Despite the prevalence of natural language processing for data mining, the majority of studies did not incorporate ML algorithms in their analyses. This preference for qualitative research methods can be attributed to the multifaceted nature of terrorism, spanning security, governance, politics, religion, and law. Our advocacy is for increased studies from the domains of geography, earth observation, and big data. Simultaneously, we encourage advancements in existing ML algorithms to enhance the accurate real-time detection of planned and onset terrorism disasters.

Compound hazards caused by tropical cyclones involve interactions among multiple hazards, such as wind, rainfall, and storm surge, significantly increasing the uncertainty and destructiveness of disasters. Existing studies primarily focus on probabilistic analyses of single or dual hazards associated with tropical cyclones, revealing limitations in handling high-dimensional data and complex dependencies. This study developed the ComHazAsTC-RRE (Compound Hazard Assessment of Tropical Cyclones within Repeatable, Reproducible, and Expandable Framework) model to analyze the compound hazards of wind, rainfall, and storm surge induced by tropical cyclones and successfully applied it to China’s coast. We collected globally accessible daily records of maximum wind speed, cumulative rainfall, and maximum storm surge for China’s coastal areas from 1979 to 2018. Using a C-Vine Copula with wind speed as the core, incorporating rainfall and storm surge as branches, we accurately captured complex dependencies of tropical cyclones. Our various return period analyses underscore the importance of considering multiple hazards and their interactions. Additionally, the application of Compound Hazard Index in China reveals that southeastern coastal areas are subjected to significantly higher compound hazards, driven by high wind speeds and strong spatial–temporal consistency of hazards. An in-depth analysis of failure probabilities indicates that neglecting the interactions among hazards can result in substantial additional cost for engineering projects, especially during severe tropical cyclones. This study offers new perspectives and scientific tools for understanding and addressing compound hazards, formulating effective disaster prevention and mitigation strategies, and supporting the sustainable development of coastal regions worldwide.

ABSTRACTEvaluating the inequity of healthcare accessibility across demographic groups in the post-COVID era is of critical importance for an aging society like Japan – it helps to achieve better social equity via distributing healthcare resources in health planning and policy making. Our study contributes to the first post-covid evaluation of multi-modal healthcare accessibility in Tokyo, Japan, the most populated metropolis in the world. A further novelty goes to the multi-dimensional examination of the inequity of healthcare accessibility (i.e. hospitals) by public transit, driving and walking – the horizontal inequity across urban space and the vertical inequity across three demographic groups (the young, adult and elderly) through network analysis, spatial accessibility analysis and inequity indexing. We find that low healthcare access areas mainly appear in the peri-urban space as well as regions less covered by public transit. Compared to the adult group, the elderly group experiences significant inequity of healthcare access particularly in the peri-urban areas where driving is the dominant transport mode to access healthcare facilities. We provide timely evidence to the Japanese government and health authorities to have a holistic and latest understanding of multi-modal healthcare access across different demographic groups in the post-COVID era.

ABSTRACTThe launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival of future GPT-x iterations have marked the beginning of the generative artificial intelligence (GAI) era. We conducted a systematic review of how to integrate LLMs including GPT and other GAI models into geospatial science, based on 293 papers obtained from four databases of academic publications – Web of Science (WoS), Scopus, SSRN and arXiv – 26 papers were eventually included for analysis. We statistically outlined the share of domains where LLMs and other GAI models, the type of data that have been used for these models, and the modelling tasks and roles that they play. We also pointed out the challenges and future directions for the next research agenda – along with which we could better position ourselves in the mainstream of science and the cutting-edge research paradigm as others leverage insights from the growing data deluge.

Abstract BackgroundAllergic diseases are associated with an increased susceptibility to respiratory tract infections. Although allergen immunotherapy (AIT) alters the course of allergies, there is limited evidence from clinical practice demonstrating its ability to enhance the host defense against pathogens. ObjectiveThe aim of this study was to investigate the protective effect of AIT against viral infection in patients with allergic rhinitis (AR) and allergic asthma (AS) based on clinical evidence. MethodsA multicenter, questionnaire-based survey was conducted during a tremendous surge in COVID-19 cases between February 10, 2023, and March 15, 2023, in 81 centers across China recruiting healthy volunteers and patients with AR and AS to investigate the clinical outcomes of COVID-19 infection. ResultsOf 10,151 participants recruited in the survey, 3654 patients and 2192 healthy volunteers who tested positive for COVID-19 were included in this analysis after screening. Overall, no significant differences in COVID-19 outcomes were observed between patients and healthy volunteers. An additional 451 patients were excluded due to their use of biologics as the sole add-on treatment, leaving 3203 patients in the further analysis. Of them, 1752 were undergoing routine medication treatment (RMT; the RMT group), whereas 1057 and 394 were receiving AIT and a combination of AIT and omalizumab (OMA) as adjunct therapies to RMT, respectively (AIT+RMT and AIT+OMA+RMT groups). The AIT group showed milder COVID-19 symptoms, shorter recovery periods, and a lower likelihood of hospitalization or emergency department visits than the RMT group (all PPPP ConclusionsOur multicenter observational study provides valuable clinical evidence supporting the protective effect of AIT against COVID-19 infection in patients with AR and AS.

ABSTRACTThe rise in human mobility data in recent years provides a new frontier to understanding the spatiotemporal dynamics of mobility to urban parks. These human mobility data offer profound merit in the study of how accessible urban parks are through real-time analytics of individual travel patterns and locations. This study systematically reviewed 46 peer-reviewed publications from the Web of Science and Academic Search Complete databases relevant to human mobility data and urban park accessibility measures. The study aims to provide a comprehensive and quantitative evaluation of the utilization of human mobility data in the field of urban park accessibility research. This objective is pursued through a systematic examination of existing literature within this research domain. We summarize the types of mobility datasets and the modelling tasks adopted. Meanwhile, we grouped the methodological/thematic frameworks from empirical use of human mobility data in park accessibility into seven categories: 1) inequality and inequity level, 2) users’ perception and exposure level, 3) frequency and variations in park visitations, 4) park service area or effective service radius, 5) happiness and sentiment level, 6) travel mode choice and trip assignment, and 7) park characteristics. Additionally, we highlighted the obstacles in integrating human mobility data into park accessibility research and engaged in a discussion surrounding prominent ethical dilemmas related to the utilization of big data.

It has been demonstrated that music can be an effective tool for shaping the future of college students. This study aimed to investigate the effects of college students’ participation in group music activities on their pro-social behavior, and for the first time introduced the psychological variables of “positive social connections” and “perceived peer support”. The results of this study show the following: (1) Group music activity participation is positively associated with pro-social behavior. (2) Positive social connections serve as a mediator between college student group music activity participation and college students’ pro-social behavior. (3) Perceived peer support serves as a mediator between college student group music activity participation and college students’ pro-social behavior. (4) Positive social connections and perceived peer support interlock to shape the relationship between group music activity participation and pro-social behavior. This study explores the intrinsic factors of college students’ participation in group music activities to promote their pro-social behavior from the dual perspectives of music psychology and behavioral psychology, provides empirical support for educational practice, emphasizes the importance of group music activities in promoting the development of college students’ pro-social behavior, and provides insights into new perspectives for college students’ overall healthy development.

Remote sensing imagery offers intricate and nuanced data, emphasizing the need for a profound understanding of the relationships among varied geographical elements and events. In this study, we explore the transitions from the image domain to the text domain by employing four state-of-the-art image captioning algorithms, i.e., BLIP, mPLUG, OFA, and X-VLM. Specifically, we investigate (1) the stability of these image captioning algorithms for remote sensing image captioning, (2) the preservation of similarity between images and their corresponding captions, and (3) the characteristics of their caption embedding spaces. The results suggest a moderate consistency across generated captions from different image captioning models, with observable variations contingent upon the urban entities presented. In addition, a dynamic relationship emerges between image space and the corresponding caption space, evidenced by their fluctuated correlation coefficient. Most importantly, patterns within the caption embedding space align with the observed land cover and land use in the image patches, reaffirming the potential of our pilot work as an impactful analytical approach in future remote sensing analytics. We advocate that integrating image captioning techniques with remote sensing imagery paves the way for an innovative data extraction and interpretation approach with diverse applications.

Understanding housing preferences is critical for successful compact city development. However, there is limited research on understanding preference heterogeneity in dwelling type choices. Using the Household Income and Labour Dynamics in Australia survey, this paper identifies the key housing and built environment characteristics associated with changes in dwelling type choice from detached houses to high-density. A latent class choice model captures the heterogeneity of dwelling type preferences within a traditionally low-density city, Brisbane, Australia. Findings reveal six household classes with distinct dwelling preferences: Class 1 (senior households without children with other family members) and Class 2 (couple families with children) in inner-city areas, Class 3 (high-income young households) and Class 4 (low-income households without children) in middle-city areas, Class 5 (low-income families with children) and Class 6 (middle-income young families without children) in outer-city areas. Residential environments with better access to educational facilities encourage Classes 3 and 6 to change to high-density living. Greater land use diversity encourages Classes 2, 3, and 6 to move towards high-density living. The findings can be used to design and improve high-density housing for targeted population groups across inner-, middle- and outer-city areas.

ABSTRACTThe provision of urban green spaces has been widely acknowledged to offer numerous benefits to individuals and communities. Recently, there has been a growing recognition of the importance of evaluating urban greenness from a human-centered perspective, partly due to the rapid development of eye-level greenness measurement. To provide a comprehensive overview of measurement approaches and topics related to eye-level urban greenness, we conducted a systematic review of 77 relevant papers. Our review revealed that street view images are the primarily utilized data source to quantify eye-level urban greenness in urban analysis. Additionally, simulated eye-level urban greenness represents a new frontier for the general public and urban planners, offering the potential to acquire universally applicable quantitative greenness data. Our findings further suggest that eye-level urban greenness has been explored across various domains, including physical behavior, public health, urban planning, human perception, and inequity. Such exploration has highlighted a significant association between eye-level urban greenness and individuals and communities. Our review generates new insights on the integration of urban greenness into urban planning and design, encouraging a greater focus on eye-level urban greenness as a critical component of urban environmental quality.

Abstract In response to the impact of COVID-19, the manufacturing industry and academic industrial research have largely shifted to online or hybrid conference formats. The sudden change has posed challenges for researchers and teams to adapt. Based on the current state of online conferences, inadequate communication, disruptions during meetings, confusion and loss of meeting information, and difficulties in conducting online collaborations are observed. This paper presents a design of a real-time discussion board that combines online conferences and synchronous discussions to address the issues arising from remote collaborations in industrial research. The research demonstrates that synchronous discussions conducted within multi-team industrial collaboration teams with specific and diverse issues can better control the flow of meetings, enhance meeting efficiency, promote participant interaction and engagement, reduce information loss, and weaken the boundaries between online and offline collaboration.

This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.

For many years, urban sciences relied heavily on traditional, authoritative data sources. However, a paradigm shift has occurred recently with the advent of citizen-driven data contribution. This evolution in data acquisition for urban science is attributable to advancements in positioning and navigation technologies, widespread use of digital devices, the rise of Web 2.0, enhanced broadband communications, and refined data management techniques. The significance of crowdsourced geospatial data in the realm of urban sciences is now widely acknowledged. A diverse array of novel data sources has been increasingly gaining prominence. These include, but are not limited to, social media platforms, street view images, GPS signals from cellphones, smartphone applications, transaction records, online mapping services, and sensor data crowdsourced from the public. This innovative approach to data collection, fuelled by contributions from individuals around the globe, opens up possibilities for accessing data that would otherwise remain untapped. This editorial aims to offer a unique perspective on how crowdsourced geospatial data is revolutionizing the field of urban sciences. We categorize the studies into five primary areas: 1) tourism, 2) urban visuals and perception, 3) urban infrastructure and functionality, 4) mobility and transportation, and 5) miscellaneous, and further discuss the challenges and opportunities that lie ahead for future research in this field.

To ensure the sustainable development of artificial intelligence (AI) application in urban and geospatial science, it is important to protect the geographic privacy, or geo-privacy, which refers to an individual’s geographic location details. As a crucial aspect of personal security, geo-privacy plays a key role not only in individual protection but also in maintaining ethical standards in geoscientific practices. Despite its importance, geo-privacy is often not sufficiently addressed in daily activities. With the increasing use of large multimodal models (LMMs) such as GPT-4 for open-source intelligence (OSINT), the risks related to geo-privacy breaches have significantly escalated. This study introduces a novel GPT-4-based model, GeoLocator, integrated with location capabilities, and conducts four experiments to evaluate its ability to accurately infer location information from images and social media content. The results demonstrate that GeoLocator can generate specific geographic details with high precision, thereby increasing the potential for inadvertent exposure of sensitive geospatial information. This highlights the dual challenges posed by online data-sharing and information-gathering technologies in the context of geo-privacy. We conclude with a discussion on the broader impacts of GeoLocator and our findings on individuals and communities, emphasizing the urgent need for increased awareness and protective measures against geo-privacy breaches in the era of advancing AI and widespread social media usage. This contribution thus advocates for sustainable and responsible geoscientific practices.

AR integrates virtual elements with the real world in real-time to enhance interactivity and vividness, which may influence consumers’ perceptions and payment intentions. This study explores the impact of Augmented Reality (AR) on consumer willingness to pay for cultural heritage products, utilizing the Technology Acceptance Model (TAM) and flow theory. This study analyzes 603 responses (quantitative data) to understand consumer perceptions of AR in the purchasing process of cultural heritage products. The findings reveal that perceived usefulness, ease of use, and flow experience significantly enhance consumer purchase intentions, with ease of use also amplifying the effects of perceived usefulness and flow experience. Additionally, the immersive, interactive, and aesthetic aspects of AR contribute positively to ease of use and flow experiences, with immersion notably impacting perceived usefulness. The results support the research model with robust explanatory power, offering practical insights for employing AR to improve marketability and consumer engagement with cultural heritage products. This paper contributes to the existing literature by bridging the gap in understanding the role of AR in enhancing consumer experiences and financial outcomes in the cultural heritage domain.

Entomopathogenic fungi are valuable sources of biological pesticides, with conidial yield and quality being pivotal factors determining their broad applications. AzaR, a fungus-specific zinc-cluster transcription factor, is known to regulate the biosynthesis of polyketone secondary metabolites in Aspergillus niger; however, its role in pathogenic fungi remains unclear. This study investigated the role of MaAzaR in the growth, development, and environmental tolerance of Metarhizium acridum. MaAzaR deletion slowed down conidial germination rate, caused reduction in conidial yield, lowered fungal tolerance to UV radiation, did not affect fungal heat-shock tolerance, and increased fungal sensitivity to the cell-wall-destructive agent calcofluor white. Furthermore, MaAzaR deletion transformed microcycle conidiation to normal conidiation on the microcycle conidiation medium. Transcription profile analysis demonstrated that MaAzaR could regulate transformation of the conidiation pattern by controlling the expression of genes related to cell division, mycelium growth and development, and cell wall integrity. Thus, this study identified a new gene related to fungal conidiation and environmental tolerance, enriching our understanding of the molecular mechanism of microcycle conidiation and providing theoretical support and genetic resources for the development of high-yielding strains.

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors.

AbstractPurpose Few risk-forecasting models of allergic rhinitis (AR) exist that may aid AR pre-exposure prophylaxis (PrEP) in clinical practice. Therefore, this study aimed to develop and validate an effective clinical model for identifying candidates for AR PrEP using a routine medical questionnaire.Methods This study was conducted in 10 Chinese provinces with 13 medical centers (n = 877) between 2019 and 2021. Clinical characteristics and exposure history were collected via face-to-face interviews. Well-trained physicians diagnosed patients with AR based on skin prick test results and clinical performance. The least absolute shrinkage and selection operator model was used to identify potential risk factors for AR, and the logistic regression model was used to construct the risk-forecasting model. Predictive power and model reliability were assessed using area under the receiver operating characteristic curve and calibration curves, respectively.Results This study diagnosed 625 patients with AR who had positive responses to at least one indoor or outdoor allergen and 460 to at least one outdoor pollen allergen. Two nomograms were established to identify two types of AR with various sensitization patterns. Both models had an area under curve of approximately 0.7 in the development and internal validation datasets. Additionally, our findings found good agreement for the calibration curves of both models.Conclusion Early identification of candidates for AR PrEP using routine medical information may improve the deployment of limited resources and effective health management. Our models showed good performance in predicting AR; therefore, they can serve as potential automatic screening tools to identify AR PrEP candidates.

ABSTRACTCOVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crises. However, the evaluation of vulnerability and healthcare accessibility is insufficient in Japan – a nation with high population density and super-aging challenges. Drawing on the 2022 census data, transport network, medical and digital cadastral data, land use maps, and points of interest data, our study extends the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan – the most populated metropolitan region in the world. We delineate the vulnerable neighbourhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability. Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level. Our analytical framework can be employed in different geographic contexts, guiding through place-based health planning and policy making in the post-COVID era and beyond.

Research on the information module has identified a range of factors in the adoption model that determine their intention to learn sustainably. This research investigates the determinants of employees’ intentions to use an online learning module within an office application, utilizing a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Data were collected from 564 employees in different organizations and analyzed using structural equation modeling. The research examines correlations between four key determinants—performance expectancy, effort expectancy, social influence, and facilitating conditions—and eight user experience motivational factors, including career development, business needs, self-management, learning experience, company atmosphere, external pressure, company support, and course quality. The findings indicate that all four UTAUT determinants significantly support employees’ use of the online learning module for continuous learning. Performance expectancy is positively influenced by career development and business needs, effort expectancy by self-management, social influence by external pressure, and facilitating conditions by company support and course quality. The learning experience and company atmosphere showed no significant correlation. The study offers valuable insights for managers, investors, and developers to enhance the adoption of online learning modules, promoting sustainable learning and employee development.

Abstract Background This cross‐sectional study aimed to identify latent sensitization profiles of asthma patients in mainland China, unveiling the association between regional differences and sensitization patterns. Methods 1056 asthma participants from 10 medical centers divided into eastern and western cohorts were clustered into four individual sensitization patterns, respectively, by using an unsupervised statistical modeling method, latent class analysis (LCA), based on the levels of 12 aeroallergens specific IgE reactivities. Moreover, differences in clinical characteristics and environmental exposures were compared in different sensitization patterns. Results Four distinct sensitization patterns in the two cohorts were defined as follows, respectively. Eastern cohort: Class 1: “High weed pollen and house dust mites (HDMs) sensitization” (8.87%), Class 2: “HDMs dominated sensitization” (38.38%), Class 3: “High HDMs and animal dander sensitization” (6.95%), Class 4: “Low/no aeroallergen sensitization” (45.80%). Western cohort: Class 1: “High weed pollen sensitization” (26.14%), Class 2: “High multi‐pollen sensitization” (15.02%), Class 3: “HDMs‐dominated sensitization” (10.33%), Class 4: “Low/no aeroallergen sensitization” (48.51%). Of note, the significant statistical difference in age, asthma control test score (ACT) and comorbidities were observed within or between different sensitization patterns. Exposure factors in different sensitization patterns were pointed out. Conclusions Asthmatic patients with distinct sensitization patterns were clustered and identified through the LCA method, disclosing the relationship between sensitization profiles of multiple aeroallergens and geographical differences, providing novel insights and potential strategies for atopic disease monitoring, management and prevention in clinical practice.

Summary: The COVID-19 pandemic has imposed catastrophic impacts on the restaurant industry as a crucial socioeconomic sector that contributes to the global economy. However, the understanding of how the restaurant industry was recovered from COVID-19 remains underexplored. This study constructs a spatially explicit evaluation of the effect of COVID-19 on the restaurant industry in the US, drawing on the attributes of +200,000 restaurants from Yelp and +600 million individual-level restaurant visitations provided by SafeGraph from 1st January 2019 to 31st December 2021. We produce quantitative evidence of lost restaurant visitations and revenue amid the pandemic, the changes in the customers’ origins, and the retained visitation law of human mobility—the number of restaurant visitations decreases as the inverse square of their travel distances—though such a distance-decay effect becomes marginal at the later pandemic. Our findings support policy makers to monitor economic relief and design place-based policies for economic recovery.

BackgroundSocial media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. ObjectiveThis study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. MethodsThis study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. ResultsTwitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P

Abstract Assessing vulnerability to natural hazards is at the heart of hazard risk reduction. However, many countries such as Australia lack measuring systems to quantity vulnerability for hazard risk evaluation. Drawing on 41 indicators from multiple data sources at the finest spatial unit of the Australian census, we re-forged the Cutter’s classic vulnerability measuring framework by involving the ‘4D’ quantification of built environment (diversity, design, density and distance), and constructed the first nationwide fine-grained measures of vulnerability for urban and rural locales, respectively. Our measures of vulnerability include five themes—(1) socioeconomic status; (2) demographics and disability; (3) minority and languages; (4) housing characteristics; and (5) built environment—that were further used to assess the inequality of vulnerability to three widely affected natural hazards in Australia (wildfires, floods, and earthquakes). We found the inequality of vulnerability in the affected areas of the three hazards in eight capital cities are more significant than that of their rural counterparts. The most vulnerable areas in capital cities were peri-urban locales which must be prioritised for hazard adaptation. Our findings contribute to the risk profiling and sustainable urban–rural development in Australia, and the broad understanding of place-based risk reduction in South Hemisphere.

In contrast to the extensive research on the impact of green buildings on character, property values, energy efficiency, and environmental quality, less emphasis has been placed on understanding the factors driving users’ selection of green real estate projects, an essential element for the future business sustainability of such buildings. This study investigates the connection between various user motivations, such as perceived usefulness, perceived usability, a sense of responsibility, awareness of consequences, and perceived behavioral control, and their impact on Asian users’ willingness to pay for green real estate projects. These users include those who have already invested in and are considering long-term investment in such projects. An empirical methodology, integrating these motivations with a technology acceptance model, is proposed to assess their willingness to pay either through renting or buying. The study applies structural equation modeling to analyze sample data against a hypothetical model, thereby testing the hypotheses. It concludes that, in influencing green real estate projects, the following apply: (1) perceived usefulness and perceived behavioral control significantly shape Asian users’ attitudes towards financial commitment; (2) perceived usability directly influences their perceived usefulness; (3) awareness of consequences acts as a mediating factor in the relationship between perceived usability and behavioral control among these users. Moreover, the study offers strategic design recommendations for future green real estate projects, informed by the diverse motivational factors of Asian users, to guide their purchasing and selection decisions.

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

Background: Metaplastic breast cancer (MBC) is a rare and aggressive form of breast cancer. The effectiveness of chemotherapy (CT) for MBC remains controversial. The present study aimed to evaluate the efficacy of CT combined hormone receptor (HR) status on MBC patients with high risk (T1-4N2-3M0 and T4N0-1M0) by propensity-score matching (PSM). Methods: A retrospective study was performed to analyze MBC from the SEER database. Breast cancer-specific survival (BCSS) was analyzed using the Kaplan–Meier curve. Cox proportional hazard models were used to assess BCSS. PSM was used to make 1:1 case-control matching. Results: This study identified 3116 patients. The median follow-up time was 44 months (range, 1–321 months). About 62.5 % of patients received CT. 23.0 % of patients were HR-positive. Recurrence risk had a significant difference between the HR-negative and HR-positive groups. In the multivariable Cox regression model, CT had no benefit for MBC patients. HR status was not associated with a better prognosis. In subgroup analysis, the Kaplan–Meier analysis showed that HR-negative MBC with intermediate-risk benefited from CT. For HR-positive MBC, patients with intermediate and high risk also benefited from CT. After PSM, neither CT nor HR status was not related to better BCSS. Moreover, the use of CT could only improve the survival of HR-positive MBC patients with high risk. Conclusion: PSM analysis showed that HR status was not associated with a better prognosis. CT was not a significant prognostic factor for prognosis. However, HR-positive MBC patients with high risk might benefit from CT.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation’s income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public’s real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space–time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement; they are also more vulnerable to risks resulting from intensive land use and land cover change, human activities, global climate change, and the rising sea levels. This study aims to predict how urban growth is affected by sea level rise (SLR) in the Australian context. We develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption strategies. Drawing on data capturing the socioeconomic and environmental factors in South East Queensland, Australia, our model is positioned to address one core research question: how does SLR affect future urban growth and human resettlement? Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation strategies. Our study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Chronic urticaria (CU) is a debilitating skin disease that lasts for more than 6 weeks with wheals and/or angioedema, including chronic spontaneous urticaria (CSU) and chronic inducible urticaria (CIndU). In China, the prevalence of this disease is high, more than 1%, and on the rise. CU has a major impact on the quality of life (QoL) of patients who frequently experience sleep disturbance, depression, and anxiety. Nearly one-third of patients with CSU, in China, are resistant to second-generation H1-antihistamines (sgAHs), even at a fourfold dose (second line; off-label). Omalizumab is approved for the treatment of CSU treatment in Europe and shows remarkable efficacy and safety. In China, regulatory approval for the use of omalizumab is pending, and its use in clinical practice varies widely. Consensus on omalizumab CU treatment in China is urgently needed. The aim of this article is to propose a practical omalizumab treatment algorithm for the management of antihistamine-resistant CSU and CIndU in adults and special population including children and adolescents, and pregnant or breast feeding women, to guide daily clinical practice in China. In the development of this consensus, an expert group including mainly dermatologists, allergists, but also pulmonologists, ENTs, immunologists, and pediatricians in Allergic Disease Prevention and Control Committee, Chinese Preventive Medicine Association, reviewed the existing evidence and developed consensus on the use of omalizumab in CU patients from China. The goal of this consensus is to assist clinicians in making rational decisions in the management of refractory CU with omalizumab. The key clinical questions covered by the treatment algorithm are: 1) Omalizumab treatment routine strategy in both CSU and CIndU patients; 2) Recommended dose and treatment duration for different age stratification; 3) Treatment duration for CU patients with other allergic comorbidities; 4) Recommendation on omalizumab stopping strategy.

BackgroundThe COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. ObjectiveThe aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. MethodsWe collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. ResultsAn increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. ConclusionsThe analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Climate change poses risks of inundation to low-lying coastal cities and may cause residential relocation and change in housing demand. Taking the City of Gold Coast in Queensland, Australia as a case study, this paper reports on a survey that investigates the potential responses of residents living in the coastal city to flood risks and how the responses may relate to their socio-economic status. Through a combined online and mail-based survey, our data show that people’s perceptions of flooding have an important impact on their relocation choices. Their perceptions and relocation choices are associated with their socio-economic background. Furthermore, residents' preferences of dwelling types appear to be affected by the level of flood risks we hypothesise in the survey. The findings from this study provide empirical evidence for future residential zoning and urban development.

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.ClinicalTrials.gov NCT00567827.