Shuxin Zhang

Accurate passenger flow prediction of urban rail transit is essential for improving the performance of intelligent transportation systems, especially during the epidemic. How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic. To solve this issue, this paper proposes a brand-new transformer-based architecture called STformer under the encoder-decoder framework specifically for COVID-19. Concretely, we develop a modified self-attention mechanism named Causal-Convolution ProbSparse Self-Attention (CPSA) to model the multiple temporal dependencies of passenger flow with low computational costs. To capture the complex and dynamic spatial dependencies, we introduce a novel Adaptive Multi-Graph Convolution Network (AMGCN) by leveraging multiple graphs in a self-adaptive manner. Additionally, the Multi-source Data Fusion block fuses the passenger flow data, COVID-19 confirmed case data, and the relevant social media data to study the impact of COVID-19 to passenger flow. Experiments on real-world passenger flow datasets demonstrate the superiority of ST-former over the other eleven state-of-the-art methods. Several ablation studies are carried out to verify the effectiveness and reliability of our model structure. Results can provide critical insights for the operation of URT systems.

Wastewater surveillance has been widely used to track the prevalence of SARS-CoV-2 in communities. Although some studies have investigated the decay of SARS-CoV-2 RNA in wastewater, understanding about its fate during wastewater transport in real sewers is still limited. This study aims to assess the impact of sewer biofilms on the dynamics of SARS-CoV-2 RNA concentration in naturally contaminated real wastewater (raw influent wastewater without extra SARS-CoV-2 virus/gene seeding) using a simulated laboratory-scale sewer system. The results indicated that, with the sewer biofilms, a 90% concentration reduction of the SARS-CoV-2 RNA was observed within 2 h both in wastewater of gravity (GS, gravity-driven sewers) and rising main (RM, pressurized sewers) sewer reactors. In contrast, the 90% reduction time was 8–26 h in control reactors without biofilms. The concentration reduction of SARS-CoV-2 RNA in wastewater was significantly more in the presence of sewer biofilms. In addition, an accumulation of c.a. 260 and 110 genome copies/cm² of the SARS-CoV-2 E gene was observed in the sewer biofilm samples from RM and GS reactors within 12 h, respectively. These results confirmed that the in-sewer concentration reduction of SARS-CoV-2 RNA in wastewater was likely caused by the partition to sewer biofilms. The need to investigate the in-sewer dynamic of SARS-CoV-2 RNA, such as the variation of RNA concentration in influent wastewater caused by biofilm attachment and detachment, was highlighted by the significantly enhanced reduction rate of SARS-CoV-2 RNA in wastewater of sewer biofilm reactors and the accumulation of SARS-CoV-2 RNA in sewer biofilms. Further research should be conducted to investigate the in-sewer transportation of SARS-CoV-2 and their RNA and evaluate the role of sewer biofilms in leading to underestimates of COVID-19 prevalence in communities.

The emergence of new variants of SARS-CoV-2 associated with varying infectivity, pathogenicity, diagnosis, and effectiveness against treatments challenged the overall management of the COVID-19 pandemic. Wastewater surveillance (WWS), i.e., monitoring COVID-19 infections in communities through detecting viruses in wastewater, was applied to track the emergence and spread of SARS-CoV-2 variants globally. However, there is a lack of comprehensive understanding of the use and effectiveness of WWS for new SARS-CoV-2 variants. Here we systematically reviewed published articles reporting monitoring of different SARS-CoV-2 variants in wastewater by following the PRISMA guidelines and provided the current state of the art of this study area. A total of 80 WWS studies were found that reported different monitoring variants of SARS-CoV-2 until November 2022. Most of these studies (66 out of the total 80, 82.5%) were conducted in Europe and North America, i.e., resource-rich countries. There was a high variation in WWS sampling strategy around the world, with composite sampling (50/66 total studies, 76%) as the primary method in resource-rich countries. In contrast, grab sampling was more common (8/14 total studies, 57%) in resource-limited countries. Among detection methods, the reverse transcriptase polymerase chain reaction (RT-PCR)-based sequencing method and quantitative RT-PCR method were commonly used for monitoring SARS-CoV-2 variants in wastewater. Among different variants, the B1.1.7 (Alpha) variant that appeared earlier in the pandemic was the most reported (48/80 total studies), followed by B.1.617.2 (Delta), B.1.351 (Beta), P.1 (Gamma), and others in wastewater. All variants reported in WWS studies followed the same pattern as the clinical reporting within the same timeline, demonstrating that WWS tracked all variants in a timely way when the variants emerged. Thus, wastewater monitoring may be utilized to identify the presence or absence of SARS-CoV-2 and follow the development and transmission of existing and emerging variants. Routine wastewater monitoring is a powerful infectious disease surveillance tool when implemented globally.

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.