Risks, Epidemics, and Prevention Measures of Infectious Diseases in Major Sports Events: Scoping Review.

BACKGROUND: Major sports events are the focus of the world. However, the gathering of crowds during these events creates huge risks of infectious diseases transmission, posing a significant public health threat. OBJECTIVE: The aim of this study was to systematically review the epidemiological characteristics and prevention measures of infectious diseases at major sports events. METHODS: The procedure of this scoping review followed Arksey and O'Malley's five-step methodological framework. Electronic databases, including PubMed, Web of Science, Scopus, and Embase, were searched systematically. The general information (ie, publication year, study type) of each study, sports events' features (ie, date and host location), infectious diseases' epidemiological characteristics (ie, epidemics, risk factors), prevention measures, and surveillance paradigm were extracted, categorized, and summarized. RESULTS: A total of 24,460 articles were retrieved from the databases and 358 studies were included in the final data synthesis based on selection criteria. A rapid growth of studies was found over recent years. The number of studies investigating epidemics and risk factors for sports events increased from 16/254 (6.3%) before 2000 to 201/254 (79.1%) after 2010. Studies focusing on prevention measures of infectious diseases accounted for 85.0% (238/280) of the articles published after 2010. A variety of infectious diseases have been reported, including respiratory tract infection, gastrointestinal infection, vector-borne infection, blood-borne infection, and water-contact infection. Among them, respiratory tract infections were the most concerning diseases (250/358, 69.8%). Besides some routine prevention measures targeted at risk factors of different diseases, strengthening surveillance was highlighted in the literature. The surveillance system appeared to have gone through three stages of development, including manual archiving, network-based systems, and automated intelligent platforms. CONCLUSIONS: This critical summary and collation of previous empirical evidence is meaningful to provide references for holding major sports events. It is essential to improve the surveillance techniques for timely detection of the emergence of epidemics and to improve risk perception in future practice.

COVID-19 Vaccination Rates of People Who Use Drugs - Chengdu City, Sichuan Province, China, November 2021 - February 2022.

What is already known about this topic?: Few studies have reported that people who use drugs (PWUDs) have much lower coronavirus disease 2019 (COVID-19) vaccination rates than the general population, especially with no relative information reported in China specifically. What is added by this report?: This study seminally uncovers that the vaccination rate among PWUDs was about 79.34% in one district of Chengdu City, Sichuan Province, China. Assuming that unvaccinated PWUDs with disease records were really not eligible for vaccination, the vaccination rate goes up to 87.25% among the studied PWUDs. The study implies that PWUDs were not left behind in the vaccination drive against COVID-19 in China. What are the implications for public health practice?: In pandemics like COVID-19, government leadership and the overall planning and distribution of public health products are critical in achieving national health equity. However, in order to do this as well as avoid discrimination or exclusion among specific portions of the general population, it's necessary to understand the vaccination rates and behaviors of at-risk groups such as PWUD's.

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images.

OBJECTIVE: To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images. METHODS: The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation, and stage-assessing AI algorithms) that were trained and tested on 313,767 CT images from 520 COVID-19 patients. This proposed method used 3D lung lesion that was segmented by the lung and lesion segmentation algorithms to extract radiomics features, and then combined with clinical metadata to assess the possible stage of COVID-19 patients using stage-assessing algorithm. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate diagnostic performance. RESULTS: Of 520 patients, 66 patients (mean age, 57 years ± 15 [standard deviation]; 35 women), including 203 CT scans, were tested. The dynamic 3D radiomics analysis method used 30 features, including 27 radiomics features and 3 clinical features to assess the possible disease stage of COVID-19 with an accuracy of 90%. For the prediction of each stage, the AUC of stage 1 was 0.965 (95% CI: 0.934, 0.997), AUC of stage 2 was 0.958 (95% CI: 0.931, 0.984), AUC of stage 3 was 0.998 (95% CI: 0.994, 1.000), and AUC of stage 4 was 0.975 (95% CI: 0.956, 0.994). CONCLUSION: With high diagnostic performance, the dynamic 3D radiomics analysis using artificial intelligence could represent a potential tool for helping hospitals make appropriate resource allocations and follow-up of treatment response. KEY POINTS: • The AI segmentation algorithms were able to accurately segment the lung and lesion of COVID-19 patients of different stages. • The dynamic 3D radiomics analysis method successfully extracted the radiomics features from the 3D lung lesion. • The stage-assessing AI algorithm combining with clinical metadata was able to assess the four stages with an accuracy of 90%, a macro-average AUC of 0.975.

A deep-learning-based framework for severity assessment of COVID-19 with CT images.

Millions of positive COVID-19 patients are suffering from the pandemic around the world, a critical step in the management and treatment is severity assessment, which is quite challenging with the limited medical resources. Currently, several artificial intelligence systems have been developed for the severity assessment. However, imprecise severity assessment and insufficient data are still obstacles. To address these issues, we proposed a novel deep-learning-based framework for the fine-grained severity assessment using 3D CT scans, by jointly performing lung segmentation and lesion segmentation. The main innovations in the proposed framework include: 1) decomposing 3D CT scan into multi-view slices for reducing the complexity of 3D model, 2) integrating prior knowledge (dual-Siamese channels and clinical metadata) into our model for improving the model performance. We evaluated the proposed method on 1301 CT scans of 449 COVID-19 cases collected by us, our method achieved an accuracy of 86.7% for four-way classification, with the sensitivities of 92%, 78%, 95%, 89% for four stages. Moreover, ablation study demonstrated the effectiveness of the major components in our model. This indicates that our method may contribute a potential solution to severity assessment of COVID-19 patients using CT images and clinical metadata.

Co-mutation modules capture the evolution and transmission patterns of SARS-CoV-2.

The rapid spread and huge impact of the COVID-19 pandemic caused by the emerging SARS-CoV-2 have driven large efforts for sequencing and analyzing the viral genomes. Mutation analyses have revealed that the virus keeps mutating and shows a certain degree of genetic diversity, which could result in the alteration of its infectivity and pathogenicity. Therefore, appropriate delineation of SARS-CoV-2 genetic variants enables us to understand its evolution and transmission patterns. By focusing on the nucleotides that co-substituted, we first identified 42 co-mutation modules that consist of at least two co-substituted nucleotides during the SARS-CoV-2 evolution. Then based on these co-mutation modules, we classified the SARS-CoV-2 population into 43 groups and further identified the phylogenetic relationships among groups based on the number of inconsistent co-mutation modules, which were validated with phylogenetic trees. Intuitively, we tracked tempo-spatial patterns of the 43 groups, of which 11 groups were geographic-specific. Different epidemic periods showed specific co-circulating groups, where the dominant groups existed and had multiple sub-groups of parallel evolution. Our work enables us to capture the evolution and transmission patterns of SARS-CoV-2, which can contribute to guiding the prevention and control of the COVID-19 pandemic. An interactive website for grouping SARS-CoV-2 genomes and visualizing the spatio-temporal distribution of groups is available at https://www.jianglab.tech/cmm-grouping/.

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.

Prevalence of Venous Thromboembolism in Critically Ill Patients With Coronavirus Disease 2019: A Meta-Analysis.

Background: Accumulating evidence suggests that coronavirus disease 2019 (COVID-19) is associated with hypercoagulative status, particularly for critically ill patients in the intensive care unit. However, the prevalence of venous thromboembolism (VTE) in these patients under routine prophylactic anticoagulation remains unknown. A meta-analysis was performed to evaluate the prevalence of VTE in these patients by pooling the results of these observational studies. Methods: Observational studies that reported the prevalence of VTE in critically ill patients with COVID-19 were identified by searching the PubMed and Embase databases. A random-effect model was used to pool the results by incorporating the potential heterogeneity. Results: A total of 19 studies with 1,599 patients were included. The pooled results revealed that the prevalence of VTE, deep venous thrombosis (DVT), and pulmonary embolism (PE) in critically ill patients with COVID-19 was 28.4% [95% confidence interval (CI): 20.0-36.8%], 25.6% (95% CI: 17.8-33.4%), and 16.4% (95% CI: 10.1-22.7%), respectively. Limited to studies, in which all patients received routine prophylactic anticoagulation, and the prevalence for VTE, DVT, and PE was 30.1% (95% CI: 19.4-40.8%), 27.2% (95% CI: 16.5-37.9%), and 18.3% (95% CI: 9.8%-26.7%), respectively. The prevalence of DVT was higher in studies with routine screening for all patients, when compared to studies with screening only in clinically suspected patients (47.5% vs. 15.1%, P < 0.001). Conclusion: Critically ill patients with COVID-19 have a high prevalence of VTE, despite the use of present routine prophylactic anticoagulation.