Space and social distancing in managing and preventing COVID-19 community spread: An overview.

The spread of COVID-19 at a large scale and at a rapid pace indicates the lack of social distancing measures at multiple levels. The individuals are not to be blamed, nor should we assume the early measures were ineffective or not implemented. It is all down to the multiplicity of transmission factors that made the situation more complicated than initially anticipated. Therefore, in facing the COVID-19 pandemic, this overview paper discusses the importance of space in social distancing measures. The methods used to investigate this study are literature review and case study. Many scholarly works have already provided us with evidence-based models that suggest the influential role of social distancing measures in preventing COVID-19 community spread. To further elaborate on this important topic, the aim here is to look at the role of space not only at the individual level but at larger scales of communities, cities, regions, etc. The analysis helps better management of cities during the pandemics such as COVID-19. By reflecting on some of the ongoing research on social distancing, the study concludes with the role of space at multiple scales and how it is central to the practice of social distancing. We need to be more reflective and responsive to achieve earlier control and containment of the disease and the outbreak at the macro level.

Campus sustainability research: indicators and dimensions to consider for the design and assessment of a sustainable campus.

Assessment Tools have become the de facto method to address sustainability issues within the built environment. They provide a measurable third-party approach to ensuring sustainability design and directives are met through the use of indictors, criteria's and credit system. A key challenge is that the existing tools such as BREEAM Communities and LEED Neighbourhood Development address a wider community but cannot efficiently address the more nuanced and context specific sustainability requirements of campuses. However, the approach of utilizing credits, indicator and criteria systems is a tactic that campus planners have slowly began to imbibe. This is even more critical to sustainable development cities due to the huge amounts of land and human resources consumed on campuses; However, gaps exist within the currently existing Campus Sustainability Assessment Tools (CSAT) that have been developed. Generally. Though studies have identified trends and gaps in neighbourhood and building assessment tools, no comprehensive study has investigated the potential gaps from the newly emerging CSATs. Using bibliometric analysis, this study reviews over 1000 articles on campus sustainability and assessment tools with the aim of determining the gaps, trends and focus area of campus sustainability via CSAT. The result draw out 15 dimensions that govern the design of sustainable campuses, and the most predominant dimensions were environmental, educational and governance dimension. The results also highlight the importance of context in implementing and developing CSATs and showed numerous overlooked campus sustainability issues, which have considerable impact on the determining the claims that makes campuses sustainable. Finally, results demonstrated a need to enhance inclusivity in decision making on campus via different survey techniques in both education and implementation of campus sustainability initiatives. In sum this study enhances the development of new CSAT and campus sustainability initiative and the modification existing initiatives to effectively meet the required sustainability standards within the built environment.

How does the COVID-19 fuel insomnia?

One of the leading health consequences of the pandemic is the prevalence of sleep-related issues, such as insomnia. Hence, this study highlights the relationship between COVID-19 and insomnia and how insomnia is increasing due to the pandemic. The study's findings summarise that the COVID-19 pandemic has produced ubiquitous mental challenges, including loneliness, anxiety, fear, stress, extreme tiredness, and health concerns. It is also associated with physical issues such as social isolation, juggling work or study, parenting challenges, and significant behavior changes stemming from mass home confinement. There are also economic hardships, financial insecurity, risk, and infection. These factors lead to undoing routines and broken circadian rhythms amid the pandemic, affecting three sleep regulatory processes of the homeostatic sleep drive, the circadian rhythm, and the arousal system. Furthermore, we suggest future research directions to explore (1) long-term health impacts of the pandemic, (2) therapeutic strategies and the implementation of social policies to support people with sleep difficulties, (3) prevention programs and clinical interventions, and (4) nationwide or cross-regional online and practical psychological and sleep management intervention systems and platforms to address the psychological strain of isolation and traumatic experiences of the pandemic.

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis.

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.