Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States.

A novel approach combining time series analysis and complex network theory is proposed to deeply explore characteristics of the COVID-19 pandemic in some parts of the United States (US). It merges as a new way to provide a systematic view and complementary information of COVID-19 progression in the US, enabling evidence-based responses towards pandemic intervention and prevention. To begin with, the Principal Component Analysis (PCA) varimax is adopted to fuse observed time-series data about the pandemic evolution in each state across the US. Then, relationships between the pandemic progress of two individual states are measured by different synchrony metrics, which can then be mapped into networks under unique topological characteristics. Lastly, the hidden knowledge in the established networks can be revealed from different perspectives by network structure measurement, community detection, and online random forest, which helps to inform data-driven decisions for battling the pandemic. It has been found that states gathered in the same community by diffusion entropy reducer (DER) are prone to be geographically close and share a similar pattern and tendency of COVID-19 evolution. Social factors regarding the political party, Gross Domestic Product (GDP), and population density are possible to be significantly associated with the two detected communities within a constructed network. Moreover, the cluster-specific predictor based on online random forest and sliding window is proven useful in dynamically capturing and predicting the epidemiological trends for each community, which can reach the highest.

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia.

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

Social Risk Factors of Transportation PPP Projects in China: A Sustainable Development Perspective.

Public-private partnerships (PPPs) have become increasingly important in improving the sustainability of society in China, with transportation being the largest investment area. However, the Social Risk Factors (SRFs) of transportation PPPs in China, which serve as a useful tool for distinguishing strengths and weaknesses for effective social risk management (SRM), have not been clearly identified. A conceptual model including 3 risk dimensions and 15 SRFs was proposed to mitigate social risks and improve the social sustainability of transportation PPP projects. A questionnaire survey conducted to investigate stakeholders’ opinions on the proposed SRFs demonstrated that all the SRFs were important. The SRFs can be used to evaluate social risks from economic, environmental, and social dimensions. Confirmatory factor analysis (CFA) verified the classification of the SRFs and indicated that all the risk dimensions contributed to social risks. The social and environmental impacts on social sustainability may contribute more to the generation of social risks. Furthermore, the concept of people-first PPPs was proposed to reduce social risks from the perspective of different stakeholders, with the interactions among different stakeholders being prioritized. The identified SRFs and their relationships can improve our understanding of SRM in the delivery of social sustainability and improve social resilience.

Prospective safety performance evaluation on construction sites.

This paper presents a systematic Structural Equation Modeling (SEM) based approach for Prospective Safety Performance Evaluation (PSPE) on construction sites, with causal relationships and interactions between enablers and the goals of PSPE taken into account. According to a sample of 450 valid questionnaire surveys from 30 Chinese construction enterprises, a SEM model with 26 items included for PSPE in the context of Chinese construction industry is established and then verified through the goodness-of-fit test. Three typical types of construction enterprises, namely the state-owned enterprise, private enterprise and Sino-foreign joint venture, are selected as samples to measure the level of safety performance given the enterprise scale, ownership and business strategy are different. Results provide a full understanding of safety performance practice in the construction industry, and indicate that the level of overall safety performance situation on working sites is rated at least a level of III (Fair) or above. This phenomenon can be explained that the construction industry has gradually matured with the norms, and construction enterprises should improve the level of safety performance as not to be eliminated from the government-led construction industry. The differences existing in the safety performance practice regarding different construction enterprise categories are compared and analyzed according to evaluation results. This research provides insights into cause-effect relationships among safety performance factors and goals, which, in turn, can facilitate the improvement of high safety performance in the construction industry.