ABSTRACT
The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment. In some buildings with high traffic flow, infected individuals release viral particles during movement. The complex interactions between humans, building, and viruses make it difficult to predict indoor infection risk by traditional computational fluid dynamics methods. The paper developed a spatially-explicit agent-based model to simulate indoor respiratory pathogen transmission for buildings with frequent movement of people. The social force model simulating pedestrian movement and a simple forcing method simulating indoor airflow were coupled in an agent-based modeling environment. The impact of architectural and behavioral interventions on the indoor infection risk was then compared by simulating a supermarket case. We found that wearing a mask was the most effective single intervention, with all people wearing masks reducing the percentage of infections to 0.08%. Among the combined interventions, the combination of customer control is the most effective and can reduce the percentage of infections to 0.04%. In addition, the extremely strict combination of all the interventions makes the supermarket free of new infections during its 8-hour operation. The approach can help architects, managers, or the government better understand the effect of nonpharmaceutical interventions to reduce the infection risk and improve the level of indoor safety.
ABSTRACT
How to improve the partial or overall performance of rail transit route network, strengthen the connection between different rail network stations, and form corresponding communities to resist the impact of sudden or long-term external factors has earned a lot of attention recently. However, the corresponding research studies are mostly based on the rail network structure, and the analysis and exploration of the community formed by the stations and its robustness are not enough. In this article, the evolution of the China rail transit route network (CRTRN) from 2009 to 2022 is taken as the research object, and its complex network characteristics, BGLL model-based community division, and multi disturbance strategies for network robustness are analyzed in depth to better understand and optimize the rail network structure to further effectively improve the efficiency of the public transport system. It is found that CRTRN is gradually expanding following the southwest direction (with the migration distance of nearly 200 km), the distribution of routes is more balanced, and the number of network communities is steadily decreasing (it dropped from 30 communities in 2009 to 25 in 2019), making various regions become closely connected. However, it can also be found that during the COVID-19 pandemic, the CRTRN is strongly affected, and the network structure becomes relatively loose and chaotic (the number of communities became 30). To protect the railway networks, the CRTRN system should pay more attention to stations with high node degree values;if they get disturbed, more areas will be affected. The corresponding research conclusions can provide some theoretical and practical support for the construction of the rail transit network in China.
ABSTRACT
The COVID-19 pandemic has lasted from 2019 to 2022, severely disrupting human health and daily life. The combined effects of spatial, environmental, and behavioral factors on indoor COVID-19 spread and their interactions are usually ignored. Especially, there is a lack of discussion on the role of spatial factors in reducing the risk of virus transmission in complex and diverse indoor environments. This paper endeavours to summarize the spatial factors and their effects involved in indoor virus transmission. The process of release, transport, and intake of SARS-CoV-2 was reviewed, and six transmission routes according to spatial distance and exposure way were classified. The triangular relationship between spatial, environmental and occupant behavioral parameters during virus transmission was discussed. The detailed effects of spatial parameters on droplet-based, surface-based and air-based transmission processes and virus viability were summarized. We found that spatial layout, public-facility design and openings have a significant indirect impact on the indoor virus distribution and transmission by affecting occupant behavior, indoor airflow field and virus stability. We proposed a space-based indoor multi-route infection risk assessment framework, in which the 3D building model containing detailed spatial information, occupant behavior model, virus-spread model and infection-risk calculation model are linked together. It is also applicable to other, similar, respiratory infectious diseases such as SARS, influenza, etc. This study contributes to developing building-level, infection-risk assessment models, which could help building practitioners make better decisions to improve the building's epidemic-resistance performance.
Subject(s)
Air Pollution, Indoor , COVID-19 , Influenza, Human , COVID-19/epidemiology , Humans , Pandemics , SARS-CoV-2ABSTRACT
OBJECTIVES: To recognise clinical features of COVID-19 pneumonia and its differences from influenza pneumonia. METHODS: 246 patients were enrolled into COVID-19 cohort and 120 patients into influenza cohort. All data were collected and analysed retrospectively. The variables under focus included demographic, epidemiological, clinical, laboratory and imaging characteristics of COVID-19 pneumonia and comparison were made with influenza pneumonia. RESULTS: The COVID-19 cohort included 53.25% female and 46.75% male. Their main symptom was fever; while 28.05% of patients had only initially fever; 21.54% of them remained feverless. After excluding prior kidney diseases, some patients showed abnormal urinalysis (32.11%), elevated blood creatinine (15.04%) and blood urea nitrogen (19.11%). Typical CT features included ground glass opacity, consolidation and band opacity, which could present as characteristic 'bat wing sign'. Our data showed that male, aged 65 or above, smoking, with comorbidities including diabetes, cardiovascular and kidney diseases, would experience more severe COVID-19 pneumonia. In comparison, COVID-19 cohort showed significantly higher incidence of clustering; the influenza cohort showed higher rate of fever. Both cohorts showed reduced lymphocyte numbers; however, 6 influenza patients showed lymphocytes increased, which was statistical significant compared with COVID-19 cohort. Also, influenza cohort displayed higher white blood cell counts and PCT values. CONCLUSION: There is no significant gender difference in the incidence of COVID-19 pneumonia. It predominantly affects the lung as well as the kidney. Age, smoking and comorbidities could contribute to disease severity. Although COVID-19 is more infectious, the rate of secondary bacterial infection is lower than influenza.