Feasibility of infection control measures in hemodialysis units to prevent outbreaks of COVID-19: A descriptive study from quebec

Background: In-center hemodialysis (HD) units pose the perfect conditions for COVID-19 transmission yet limited space and resources are obstacles to infection prevention and control (IPAC) measures. We aimed to describe IPAC measures implemented and document the infection rates within HD units during the first year of the pandemic. Methods: We invited leaders of Quebec's HD units to collect information on IPAC measures from March 1st to June 30th 2020 and HD unit characteristics. Participating units were contacted again in March 2021 to collect information about the total number of cases. The cumulative infection rate of each unit was compared to the regional cumulative infection rate using a standardized infection ratio (SIR). Results: Data was obtained from 38 units, representing 90% of Quebec's HD patients. 30% of units were perceived as crowded, and this was associated with objective distance measures between stations, which was much more likely to be <2m in units considered crowded (83.3% vs 19.2% p<0.001). IPAC measures regarding general prevention, screening procedures, physical distancing, and PPE use were implemented in 50% of units by 3 weeks and the remainder by 6 weeks. Data on cumulative infection rate was obtained in 26 units providing care to 3942 patients. The cumulative infection rate was disproportionally elevated in HD units compared to regional rates (Median SIR:2.68 IQR:1.58;4.45)(Figure 1). No difference was noted in the SIR related to specific IPAC measures or to the physical characteristics of the units. Conclusions: Hemodialysis units throughout Quebec were able to rapidly implement modified IPAC measures. Despite this, infection rates were disproportionally elevated.

Depth embedded and dense dilated convolutional network for crowd density estimation

In recent years, due to the rapid growth of the urban population, the management of public security has become extremely necessary. Therefore, accurate crowd counting and density distribution estimation play an important role in many situations especially during the Covid-19 pandemic which has been spreading around the world. Although many studies have been proposed, it remains to be a challenging task because of the vivid intra-scene scale variations of people caused by depth effects. In this paper, we propose a novel unified system that allows the scale variation problem to be solved both directly and indirectly. To allow the network to have an understanding of depth when estimating crowd density, we first propose to embed this information into the crowd density estimation network indirectly through the training process by mean of multi-task learning. Our network is now designed to solve not only the main task of estimating crowd density, but also a side task: depth estimation. Besides, to learn the large-scale features directly, dense dilated convolution blocks were proposed to be used in our encoder. The experimental results demonstrate that by using both such direct and indirect methods, we can boost the performance and achieve good results compared to existing methods. Besides, with the multi-task design, we can completely cut off the unnecessary branches of the network related to the side task to speed up computation during the testing phase. © 2021 IEEE.