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Many intervention strategies, such as patient isolation and contact tracking, had been implemented in different countries to slow down and control the spread because of the enormous threats and losses caused by COVID-19. Since the contact relationships of millions of people within cities change over time, it is difficult to accurately predict the dynamics of large-scale outbreaks and evaluate the impact of the contact tracking and isolation strategy based on individual contact history on epidemic transmission. Here, we propose a non-markov spreading model based on individual contact dynamic network, to simulate the dynamic contact processes in a city with millions of people. In this model, the historical contact population of each infected person can be backtracked and tracked. Our model can accurately describe the COVID-19 epidemic in Wuhan and Hong Kong. We assess the impact of four agent-based epidemic intervention strategies: travel control, contact tracking and isolation, vaccination, and regular nucleic acid testing to all residents on the epidemic evolution and economic losses. We find that for the original SARS-CoV-2 virus, a strict travel control strategy is effective in both suppressing the spread of COVID-19 and minimizing economic losses. For the Omicron variant (BA.2) with stronger infectious capacity, a relatively loose travel control and an appropriate combination of the other three strategies can effectively control the epidemic outbreak while minimize economic losses. This paper provides an efficient framework for assessing the combination of different agent-based strategies by large-scale simulations in the case of unknown historical contact information of large populations, and the studies on different combinations of control strategies can provide theoretical guidance for future prevention and control. • We propose a non-markov spreading model based on individual contact dynamic network. • Our model can accurately describe the COVID-19 epidemic in Wuhan and Hong Kong. • We assess the impact of four agent-based epidemic intervention strategies. • A strict travel control strategy may lead to relatively high economic losses. • Tracking strategy can control the epidemic outbreak while minimize economic losses. [ABSTRACT FROM AUTHOR]

Rapid design and construction of mobile cabin hospitals (MCHs) have become imperative in the COVID-19 response. However, due to unique design specifications (e.g., parallel design and model pre-revision), collaboration in emergency construction projects (ECPs) like MCHs presents data security vulnerabilities, including a lack of traceability and transparency. These hazards invariably reduce design effectiveness, leading to undesirable rework and project delay. Blockchain technology is a potential solution to address the aforementioned security issues in ECPs because it offers immutable and traceable data storage. Nevertheless, directly implementing blockchain in ECPs is impractical, for the blockchain has a complex deployment process and provides limited functions supporting BIM-based design. Therefore, this paper develops a lightweight blockchain-as-a-service (LBaaS) prototype to enhance the ECPs design efficiency by securing and automating information exchange while eliminating the difficulties of deploying and using blockchain. This paper contributes three elements: (1) Security vulnerabilities of design in ECP are identified. Taking an MCH in Hong Kong as an example, this paper investigates its design process and determines two design characteristics and associated security flaws. (2) Key technologies to support easy deployment and usage of blockchain in ECPs are developed. New technical elements, including a Multi-to-One mapping (MtOM) kit for easy blockchain registration, an integrated workflow retaining existing design practices, and smart contracts for secure interaction with blockchain, are developed to support LBaaS functionality. (3) An LBaaS prototype is validated and evaluated. The prototype is illustrated and evaluated using design examples based on actual MCH project data. Results show that the LBaaS is a feasible and secure approach for ECPs collaboration. This paper deepens the understanding of data security issues in ECPs and offers technical guidance in establishing blockchain solutions. • Identified security vulnerabilities of BIM-based collaboration in emergency construction projects (ECPs). • Exploring the mechanism of integrating ECPs design with a lightweight blockchain-as-a-service (LBaaS). • Developed key technical elements to support functionalities of an LBaaS prototype. • Validated LBaaS prototype via three design examples from an actual mobile cabin hospital project. • Results show that the LBaaS is a practical solution for improving data security and ECP design efficiency. [ABSTRACT FROM AUTHOR]

Covid-19 has wreaked havoc worldwide. To mitigate the spread of the virus, societies around the world implemented various social control measures and restrictions on mobility. Traditional work practices were upended, leading to a widespread adoption of telecommuting in many cities, effectively turning urban spaces into expansive work-from-home laboratories. The exchange of digitized documents, virtual conversations, and online transactions replaced physical movements and face-to-face communication, allowing for the continuation of productive activities. This shift to telecommuting raises important questions about the impact on traditional business practices and power dynamics in the workplace. It also prompts us to examine how new communication and surveillance tools facilitate collaboration and enhance worker productivity. Furthermore, it is essential to understand how employees navigate and adapt to the opportunities and limitations presented in a teleworking environment. To gain insights into these issues, this paper focuses on the experiences of work-from-home employees in the community of Tin Shui Wai in Hong Kong, using the concept of governmobility as an analytical framework. The findings reveal that telecommuting is enabled by innovative social interaction mechanisms, changes in power dynamics, and the implementation of new discourse and surveillance methods, all of which are contingent on specific work and life conditions. The various experiences of telecommuting, such as working in a home office, in virtual workspaces, and using on-body devices, demonstrate adaptable responses to the emerging opportunities and constraints of telecommuting. These experiences illuminate the intricate ways in which work and life intertwine in both physical and temporal dimensions. • Exploring the mechanisms of governmobility in telecommuting during the Covid-19 pandemic. • Theorizing diverse telecommuting practices and experiences. • Discussing the prospects of telecommuting in the post-Covid world. [ABSTRACT FROM AUTHOR]

Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. • New two-layer SIR propagation model with an infected medium for financial shocks. • Two-layer Granger networks based on the US stock markets, the mainland China stock markets, and the Hong Kong stock market. • Focusing on economic influence caused by COVID-19 epidemic. • Monte Carlo algorithm simulates the model in different conditions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially—most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts. • COVID-19 pandemic sparked calls for a new digital infrastructure. • We compare 2003 SARS outbreak to COVID-19 pandemic. • New digital technologies increased governments' ability to surveil and segregate. • Their impact on surveillance and segregation will persist post COVID-19. [ABSTRACT FROM AUTHOR]

• A new integrated approach is proposed for influenza forecasting in Hong Kong. • Stacked autoencoder is applied to Google search data for dimension reduction. • Variational mode decomposition is used to decompose the data into different modes. Forecasting influenza epidemics has important practical implications. However, the performance of traditional methods adopting in Hong Kong influenza forecasting is limited due to its particularity. This paper proposes an integrated approach for Hong Kong influenza epidemics forecasting. The novelties of our approach mainly include: firstly, we adopt a model for Google search queries data collection and selection in Hong Kong to substitute Google Correlate. Secondly, we adopt the stacked autoencoder (SAE) to reduce the dimensionality of Google search queries data. Thirdly, we adopt a signal decomposition method named variational mode decomposition (VMD) to decompose the influenza data into modes with different frequencies, which can extract the characteristic. Fourthly, we use artificial neural networks (ANN) to forecast these modes of influenza epidemics extracted by VMD respectively, then these forecasts of each mode are added to generate the final forecasting results. From the perspective of forecasting accuracy and hypothesis tests, the empirical results show that our proposed integrated approach SAE-VMD-ANN significantly outperforms some other benchmark models both in the whole period and influenza season. The performance of our proposed model during the COVID-19 pandemic is checked too. [ABSTRACT FROM AUTHOR]

Is it possible to control the covid-19 pandemic in large cities like Hong Kong? Many cities have adopted various mitigation measures to contain the covid-19 pandemic. But few studies have been made to measure the impact of mitigation measures on infection risk at city level such as Hong Kong. This paper introduced three indicators to measure the infection risk of covid-19 under mitigation measures: the infection rate, the primary risk of infection and daily risk of infection. Two factors are introduced to consider the impact of mitigation measures on infection risk in Hong Kong. They are the number of trips per day and the percentage of people wearing face masks. With these two mitigation measures, the daily risk of infection was reduced from 1826.11 per million to 644.58 per million in the peak of covid-19 infection on 2 August 2020. The covid-19 infection risk would be 2.83 times higher if above mitigation measures were not adopted. The covid-19 pandemic continues in 2021 and city governments are strongly recommended to take effective measures to encourage the public to reduce unnecessary trips and wear face mask before the pandemic is fully controlled. • A measure of daily risk of covid-19 infection is introduced. • It is possible to control the covid-19 pandemic in large cities like Hong Kong. • The peak of infection risk would be 2.83 times higher without mitigation measures in Hong Kong. [ABSTRACT FROM AUTHOR]