From SARS to COVID-19: Digital infrastructures of surveillance and segregation in exceptional times

Spatio-temporal evolution and drivers of coupling coordination between digital infrastructure and inclusive green growth: Evidence from the Yangtze River economic belt

The change of digital technology has ushered in a new era of digital infrastructure (DI) development. Facilitating the synergistic of DI and inclusive green growth (IGG) is essential for achieving sustainable development at the regional level. This study draws on panel data from 107 cities within China's Yangtze River Economic Belt (YREB) and employs a variety of research methodologies, including the entropy method, the coupling coordination degree model (CCDM), exploratory spatial data analysis (ESDA), the Dagum Gini coefficient, the GM (1.1), and an optimal parameters-based geographical detector (OPGD). The aim is to explore the coupling coordination degree (CCD) and drivers between DI and IGG from 2011 to 2020. The findings reveal: (1) Throughout the study, the CCD within the YREB remained generally low, achieving only low coordination by 2020. However, the system demonstrates greater harmonization and improved quality each year. Spatially, the distribution pattern exhibits a distinct "high in the east and low in the west" trend. (2) The CCD exhibits a positive spatial correlation, particularly with High-High clusters concentrated in the Yangtze River Delta (YRD). (3) Utilizing difference analysis and gray model predictions, the CCD level of the YREB shows considerable potential for development, with regional disparities gradually narrowing. (4) While the dominant driving factors of CCD vary across different sub-regions of the YREB, information supportability and economic driving force generally emerge as the primary drivers across different spatial sub-regions, with their impact significantly enhanced when interacting with other factors. Consequently, this study accurately identifies the driving factors in different spatial sub-divisions and suggests tailored development strategies and measures to provide more scientifically grounded policy insights.

Simulated misuse of large language models and clinical credit systems

In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.

Simulated Misuse of Large Language Models and Clinical Credit Systems

Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources via unfair, unjust, or inaccurate criteria. For example, a social credit system uses big data to assess "trustworthiness" in society, penalizing those who score poorly based on evaluation metrics defined only by a power structure (e.g., a corporate entity or governing body). Such a system may be amplified by powerful LLMs which can evaluate individuals based on multimodal data - financial transactions, internet activity, and other behavioral inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty or other rights via a "clinical credit system", which may include limiting access to care. The results of this study show that LLMs may be biased in favor of collective or systemic benefit over protecting individual rights, potentially enabling this type of future misuse. Moreover, experiments in this report simulate how clinical datasets might be exploited with current LLMs, demonstrating the urgency of addressing these ethical dangers. Finally, strategies are proposed to mitigate the risk of developing large AI models for healthcare.

Outbreak preparedness and response strategies in ASEAN member states: a scoping review

Objectives

The 21st century has witnessed significant disease outbreaks with severe impact in Association of Southeast Asian Nations (ASEAN) countries, including SARS, H1N1, H5N1, and COVID-19. This review aimed to compile and analyze outbreak preparedness and response strategies, highlighting the success of coordinated multi-sectoral approaches and policy responses within the ASEAN region.

Methods

The protocol for this review was registered on the Open Science Framework and PROSPERO. A systematic analysis of publications from the 2002-2022 period was conducted following PRISMA guidelines on 4522 records retrieved from PubMed, CINAHL, Web of Science, and Scopus. The titles and abstracts were screened, and 229 articles were selected for full-text screening. Finally, 34 articles were included in this review.

Results

Four preparedness pillars were identified: governance and stewardship, disease detection, disease prevention, and health care management. The pillars were crucial in preparing for and responding to the COVID-19 pandemic. Coordinated responses among the ASEAN countries and local and international stakeholders were reported.

Conclusions

The findings emphasize that understanding the transmission dynamics of infectious diseases is paramount for effective disease prevention, surveillance, and timely response efforts to prevent the next pandemic. A well-coordinated multi-country and multi-agency policy response and understanding the different disease management models are crucial in addressing future outbreaks in the region. Future post-pandemic publications will shed more light on lessons learned and preparedness and response plans for future pandemics.

Spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering emerging industries in Chinese cities

New infrastructure construction stemming from the new waves of technological revolution worldwide is exemplified by 5G base stations, big data centers, and ultra-high voltage. It has aroused extensive academic and policy interests in recent years, especially due to its beneficial role in empowering regional novel economic dynamics. However, this argument is still too general to capture the nuanced effects of new infrastructure construction on fostering emerging industries in specific spatial-temporal and industrial contexts, which is left for geographers to take up. This paper focuses on the spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering four distinctive emerging industries in major Chinese cities over the last decade. It reveals that new infrastructure construction and emerging industries have experienced rapid development in major Chinese cities, with geographical agglomeration in national central cities with advanced economic development level. It is empirically demonstrated that new infrastructure construction can facilitate the development of emerging industries in major Chinese cities, while significant spatial-temporal heterogeneity characterizes the contributory forces. Furthermore, artificial intelligence as a Key Enabling Technology, robotics as a kind of hardware-featured industry, software-as-a-service as a software-centered industry, and blockchain as a networking-oriented industry vary markedly in the extent and the ways in which they benefit from new infrastructure construction, and they consequently exhibit industrial sensitivity to spatial-temporal heterogeneity in the fostering effects.

The road to recovery: Sensing public opinion towards reopening measures with social media data in post-lockdown cities

The COVID-19 pandemic has resulted in cities implementing lockdown measures, causing unprecedented disruption (e.g. school/shop/office closures) to urban life often extending over months. With the spread of COVID-19 now being relatively contained, many cities have started to ease their lockdown restrictions by phases. Following the phased recovery strategy proposed by the UK government following the first national lockdown, this paper utilises Greater London as its case study, selecting three main reopening measures (i.e., schools, shops and hospitality reopening). This paper applies sentiment analysis and topic modelling to explore public opinions expressed via Twitter. Our findings reveal that public attention towards the reopening measures reached a peak before the date of policy implementation. The attitudes expressed in discussing reopening measures changed from negative to positive. Regarding the discussed topics related to reopening measures, we find that citizens are more sensitive to early-stage reopening than later ones. This study provides a time-sensitive approach for local authorities and city managers to rapidly sense public opinion using real-time social media data. Governments and policymakers can make use of the framework of sensing public opinion presented herein and utilise it in leading their post-lockdown cities into an adaptive, inclusive and smart recovery.

The Impact of Digital Economy on the Economic Growth and the Development Strategies in the post-COVID-19 Era: Evidence From Countries Along the "Belt and Road"

The digital economy is considered as an effective measure to mitigate the negative economic impact of the Corona Virus Disease 2019 (COVID-19) epidemic. However, few studies evaluated the role of digital economy on the economic growth of countries along the "Belt and Road" and the impact of COVID-19 on their digital industries. This study constructed a comprehensive evaluation index system and applied a panel data regression model to empirically analyze the impact of digital economy on the economic growth of countries along the "Belt and Road" before COVID-19. Then, a Global Trade Analysis Project (GTAP) model was used to examine the impact of COVID-19 on their digital industries and trade pattern. Our results show that although there is an obvious regional imbalance in the digital economy development in countries along the "Belt and Road", the digital economy has a significantly positive effect on their economic growth. The main impact mechanism is through promoting industrial structure upgrading, the total employment and restructuring of employment. Furthermore, COVID-19 has generally boosted the demand for the digital industries, and the impact from the demand side is much larger than that from the supply side. Specifically, the digital industries in Armenia, Israel, Latvia and Estonia have shown great growth potential during the epidemic. On the contrast, COVID-19 has brought adverse impacts to the digital industries in Ukraine, Egypt, Turkey, and the Philippines. The development strategies are proposed to bridge the "digital divide" of countries along the "Belt and Road," and to strengthen the driving effect of the digital economy on industrial upgrading, employment and trade in the post-COVID-19 era.