The effectiveness of travel restriction measures in alleviating the COVID-19 epidemic: evidence from Shenzhen, China.

With the expansion of the global novel coronavirus disease (COVID-19) pandemic, unprecedented interventions have been widely implemented in many countries, including China. In view of this scenario, this research aims to explore the effectiveness of population mobility restriction in alleviating epidemic transmission during different stages of the outbreak. Taking Shenzhen, a city with a large immigrant population in China, as a case study, the real-time reproduction number of COVID-19 is estimated by statistical methods to represent the dynamic spatiotemporal transmission pattern of COVID-19. Furthermore, migration data between Shenzhen and other provinces are collected to investigate the impact of nationwide population flow on near-real-time dynamic reproductive numbers. The results show that traffic flow control between populated cities has an inhibitory effect on urban transmission, but this effect is not significant in the late stage of the epidemic spread in China. This finding implies that the government should limit international and domestic population movement starting from the very early stage of the outbreak. This work confirms the effectiveness of travel restriction measures in the face of COVID-19 in China and provides new insight for densely populated cities in imposing intervention measures at various stages of the transmission cycle.

Relationships between building attributes and COVID-19 infection in London.

In the UK, all domestic COVID-19 restrictions have been removed since they were introduced in March 2020. After illustrating the spatial-temporal variations in COVID-19 infection rates across London, this study then particularly aimed to examine the relationships of COVID-19 infection rates with building attributes, including building density, type, age, and use, since previous studies have shown that the built environment plays an important role in public health. Multisource data from national health services and the London Geomni map were processed with GIS techniques and statistically analysed. From March 2020 to April 2022, the infection rate of COVID-19 in London was 3,159.28 cases per 10,000 people. The spatial distribution across London was uneven, with a range from 1,837.88 to 4,391.79 per 10,000 people. During this period, it was revealed that building attributes played a significant role in COVID-19 infection. It was noted that higher building density areas had lower COVID-19 infection rates in London. Moreover, a higher percentage of historic or flat buildings tended to lead to a decrease in infection rates. In terms of building use, the rate of COVID-19 infection tended to be lower in public buildings and higher in residential buildings. Variations in the infection rate were more sensitive to building type; in particular, the percentage of residents living in flats contributed the most to variations in COVID-19 infection rates, with a value of 2.3%. This study is expected to provide support for policy and practice towards pandemic-resilient architectural design.

Detection of multiple lesions of gastrointestinal tract for endoscopy using artificial intelligence model: a pilot study.

BACKGROUND: This study was aimed to develop a computer-aided diagnosis (CAD) system with deep-learning technique and to validate its efficiency on detecting the four categories of lesions such as polyps, advanced cancer, erosion/ulcer and varices at endoscopy. METHODS: A deep convolutional neural network (CNN) that consists of more than 50 layers were trained with a big dataset containing 327,121 white light images (WLI) of endoscopy from 117,005 cases collected from 2012 to 2017. Two CAD models were developed using images with or without annotation of the training dataset. The efficiency of the CAD system detecting the four categories of lesions was validated by another dataset containing consecutive cases from 2018 to 2019. RESULTS: A total of 1734 cases with 33,959 images were included in the validation datasets which containing lesions of polyps 1265, advanced cancer 500, erosion/ulcer 486, and varices 248. The CAD system developed in this study may detect polyps, advanced cancer, erosion/ulcer and varices as abnormality with the sensitivity of 88.3% and specificity of 90.3%, respectively, in 0.05 s. The training datasets with annotation may enhance either sensitivity or specificity about 20%, p = 0.000. The sensitivities and specificities for polyps, advanced cancer, erosion/ulcer and varices reached about 90%, respectively. The detect efficiency for the four categories of lesions reached to 89.7%. CONCLUSION: The CAD model for detection of multiple lesions in gastrointestinal lumen would be potentially developed into a double check along with real-time assessment and interpretation of the findings encountered by the endoscopists and may be a benefit to reduce the events of missing lesions.

Increases in noise complaints during the COVID-19 lockdown in Spring 2020: a case study in Greater London, UK

Many cities around the world have claimed that the enforcement of lockdown measures to contain the spread of COVID-19 and the corresponding limitations of human activities led to reduced environmental noise levels. However, noise complaints reported by many local authorities were on the rise soon after the local lockdowns came into force. This research took Greater London in the UK as a case study. The overall aim was examining how noise complaints changed during the first stages of the lockdown implementation, during Spring 2020, both locally and at city scale, and how urban factors may have been influencing them. Noise complaint and urban factor datasets from the Government’s publicly available data warehouse were used. The results show that during the COVID-19 lockdown the number of noise complaints increased by 48%, compared with the same period during Spring 2019. In terms of noise sources, complaints about construction (36%) and neighbourhood (50%) noise showed significant increases. Urban factors, including housing and demographic factors, played a more significant role than the actual noise exposure to road and rail traffic noise, as derived from the London noise maps. In detail, the change rate of noise complaints is higher in areas with higher unemployment rates, more residents with no qualifications, and lower house price. It is expected that this study could help government with allocating resources more effectively and achieve a better urban environment.

Assessing the changing urban sound environment during the COVID-19 lockdown period using short-term acoustic measurements

The implementation of lockdown measures due to the COVID-19 outbreak has resulted in wide-ranging social and environmental implications. Among the environmental impacts is a decrease in urban noise levels which has so far been observed at the city scale via noise mapping efforts conducted through the framework of the Environmental Noise Directive. This study aims to understand how lockdown measures have manifested at a local level to better determine how the person-level experience of the urban soundscape has been affected and how these affects differ across urban space typologies. Taking London as a case study, a series of 30-second binaural recordings were taken at 11 locations representing a cross-section of urban public spaces with varying compositions of sound sources during Spring 2019 (pre-lockdown, N = 620) and Spring 2020 (during-lockdown, N = 481). Five acoustic and psychoacoustic metrics (LAeq, LA10, LA90, Loudness, Sharpness) were calculated for each recording and their changes from the pre-lockdown scenario to the lockdown scenario are investigated. Clustering analysis was performed which grouped the locations into 3 types of urban settings based on their acoustic characteristics. An average reduction of 5.4 dB (LAeq) was observed, however significant differences in the degree of reduction were found across the locations, ranging from a 10.7 dB to a 1.2 dB reduction. This study confirms the general reduction in noise levels due to the nationally imposed lockdown measures, identifies trendswhich vary depending on the urban context and discusses the implications for the limits of urban noise reduction.