Quantifying Mobility Perturbation in America's Cities during COVID-19: A Network-Based Approach

The pandemic of COVID-19 has caused severe disruptions in urban lives. Understanding and quantifying these disruptions is important to inform the development of targeted and effective measures to control the pandemic and its impact. One way of achieving this object is to measure the urban mobility perturbation caused by the pandemic. In this study, we built mobility-based networks for seven major metropolitan statistical areas (MSAs) across the United States in the years of 2019 and 2020, respectively. We quantified the disruptions of urban mobility by computing and comparing a set of network-based metrics before and during the pandemic. The proposed approach is able to uncover the impact of COVID-19 in cities and provides new insights into the resilience of cities when facing large-scale disasters.

Effects of Precipitation on the Air Quality Index, PM2.5 Levels and on the Dry Deposition of PCDD/Fs in the Ambient Air

The effects of 9 precipitation events in Suzhou City in Anhui Province, China, on the air quality index (AQI), PM2.5, and dry deposition flux of PCDD/Fs (polydibenzo-p-dioxins and polydibenzofurans) were investigated. A total of 7 precipitation events were positive contributes to the reduction of AQI;among them, the AQI were between 23 and 216, with an average of 75, the PM2.5 concentrations were between 5.0 and 169 mu g m-3, with an average of 25 mu g m-3, while the total-PCDD/F-TEQ dry deposition flux ranged from 149 to 1034 pg WHO2005-TEQ m-2 day-1 and averaged 315 pg WHO2005-TEQ m-2 day-1. By comparing the average AQI and PM2.5, respectively, during and after rainfall with that before rainfall, the results indicated that the average reduction fractions of AQI were 26% and 44%, respectively, while those of PM2.5 were 58% and 43%. In addition, the effect of precipitation on the average reduction fraction of total PCDD/F-TEQ dry deposition flux was 31%. However, in the other 2 AQI elevation events, the AQI were between 23 and 100, and averaged 51;when comparing the average AQI and PM2.5 concentrations, during and after the rain with that before the rain, the increases in AQI were 42% and 49%, respectively, while the increases in PM2.5 concentration were 26% and 29%, respectively. The above results show that, on the whole, rain and snow improved the air quality. This is because rainwater removes particles or dissolved gaseous pollutants from the atmosphere and brings aerosols to the ground. However, in some cases, the increase of source emissions and atmospheric vertical convection, the effect of precipitation or air humidity increased the AQI and elevated the concentration of PM2.5, and dry deposition flux of PCDD/Fs. The results of this study provide useful information for both scientific communities and air quality management.

[Application of mathematical models of infectious diseases in the evaluation of COVID-19 transmission risk at mass gatherings].

The SARS-CoV-2 Omicron variant is rampant in Europe and the United States, and the Delta variant has caused several small-scale outbreaks in China. It is particularly important to simulate the transmission risk of novel coronavirus pneumonia (COVID-19) during large-scale events, so as to ensure a good preparation of personnel, materials, isolation sites and other support work in advance. Taking the Beijing 2022 Winter Olympic Games as an example, this study introduces the use of mathematical models to simulate the entry risks, closed-loop risks and prevention and control measures of athletes, officials and other stakeholders of the Olympic Games. In the simulation results on January 19, 2022, the estimated number of Olympic Games-related infections who were identified at borders was 357 (95%CI: 153-568) and the observed number was 323. The estimated number of "seed" cases that entered the closed-loop of Olympics Games was 195 (95%CI: 43-335), and the observed number of cases in the closed-loop was 212. This study demonstrates the important role of mathematical models of infectious diseases in the pragmatic application of preventive medicine and public health.

TOWARDS UNBIASED COVID-19 LESION LOCALISATION AND SEGMENTATION VIA WEAKLY SUPERVISED LEARNING

Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labelling costs, we propose a data-driven framework supervised by only image level labels. The framework can explicitly separate potential lesions from original images, with the help of an generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrates the effectiveness of the proposed framework and its superior performance to several existing methods.