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1.
Gongcheng Kexue Xuebao/Chinese Journal of Engineering ; 44(6):1080-1089, 2022.
Article in Chinese | Scopus | ID: covidwho-1876199

ABSTRACT

With the increasing popularity of the Internet and the spread of COVID-19, epidemic-related rumors have attracted significant attention, allowing them to brew quickly and pose extremely negative social impacts. It is of great significance to investigate the propagation process of online rumors and offer tentative strategies to curb it. Based on the traditional susceptible, infected, recovered (SIR) model of online rumor propagation, groups of potential and die-hard rumor believers were introduced in this paper, establishing an authoritative rumor-refuting mechanism. Meanwhile, this paper considered factors such as the time-lag effect of rumor refutation from the nonauthoritative and authoritative institutions and the impact of the popularizing rate of higher education on the propagation and refutation of rumors. As a result of the process, the SEIRD (susceptible, exposed, infected, recovered, die-hard-infected) rumor propagation model was established to study how the proportion of the susceptible, exposed, infected, recovered, and die-hard-infected varies under different popularizing rates of higher education, the presence or absence of the authoritative rumor-refuting institutions, and the time-lag effect of rumor refutation. Finally, the model's effectiveness was verified via experimental simulation, which provided a reference for controlling the spread of online rumor propagation. In addition, the paper proposed a rumor-refuting coefficient to measure the rumor-refuting ability of the nonauthoritative and authoritative institutions. The results show that (1) increasing popularizing rate of higher education significantly slows down the rumor propagation and reduces the rumor propagation peak;(2) refuting the rumors based on the authoritative institutions is decisive for the ultimate elimination of rumors;and (3) eliminating the time-lag effect in refuting rumors facilitates slowing down the propagation of the online rumors. Therefore, the paper puts forward a feasible strategy to eliminate the time-lag effect of online rumor refutation in the future. Copyright ©2022 Chinese Journal of Engineering. All rights reserved.

2.
2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1874716

ABSTRACT

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period. © 2022 Owner/Author.

3.
2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022 ; : 306-309, 2022.
Article in English | Scopus | ID: covidwho-1861136

ABSTRACT

Under the serious influence of COVID-19, online teaching has become a mainstream teaching mode. During the online teaching, it is difficult for teachers to evaluate and intervene in students' learning in real time. Therefore, for students who lack self-control, it is possible to be stuck in low learning efficiency and even failure of course assessment. How to obtain valid information of students' learning status in time during the online teaching process is a hot research topic at present. This paper proposes a feedback service for teaching based on educational data mining. It can, through a reasonable analysis of the data submitted in form of students' homework, accurately screen out students who have difficulties in learning a certain course and give directions to achieve the purpose of optimizing the teaching. © 2022 IEEE.

4.
British Journal of Social Work ; : 19, 2021.
Article in English | Web of Science | ID: covidwho-1852945

ABSTRACT

The prevalence of child maltreatment is quite high during the coronavirus disease 2019 pandemic in rural Hubei province, and children from vulnerable families are at greater risk of self-harm behaviours. The impact of lockdown measures in Wuhan, China during the coronavirus disease 2019 pandemic on child maltreatment remains unknown. The present study attempted to estimate the prevalence of child maltreatment during this period, to identify risk factors, and the influence of child maltreatment. A representative sample of 1,062 school-aged children in rural Hubei province was surveyed. Results indicated that the prevalence of family violence, physical violence, emotional abuse and neglect during the lockdown period were 13.9, 13.7, 20.2 and 7.3 percent, respectively, and that of lifetime prevalence were 17.0, 13.9, 14.6 and 6.9 percent, respectively. And most victims did not seek official help. Boys were more likely to experience physical violence. Children from separated/divorced families tended to report more emotional abuse. Those having family members with a history of drug abuse and mental illness were more likely to experience neglect during the lockdown period. All types of child maltreatment were positively associated with self-harm behaviours. These findings highlight the importance of identifying at-risk children immediately and implementing timely intervention programmes to prevent self-harm behaviours for social workers and health professionals.

5.
Optics and Biophotonics in Low-Resource Settings VIII 2022 ; 11950, 2022.
Article in English | Scopus | ID: covidwho-1846314

ABSTRACT

Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance. © 2022 SPIE

6.
Chinese Journal of Disease Control and Prevention ; 26(2):193-199, 2022.
Article in Chinese | EMBASE | ID: covidwho-1822639

ABSTRACT

Objective To investigate the willingness and influencing factors with novel coronavirus vaccines(COVID-19 vaccines) among college students in Shanghai. Methods From February 23 to March 15, 2021, a web based questionnaire survey was conducted among students from four colleges to analyze the willingness rate of COVID-19 vaccines. Multivariate Logistic regression was used to analyze influencing factors of the willingness to receive vaccines. Results Of 4 462 subjects, 78.04% were willing to receive COVID-19 vaccines. Logistic regression analysis showed that students from the technology university and the vocational school had higher willingness to vaccinate (OR=1.53, 1.50), compared with those from medical college. Respondents did not agree that vaccines are important for protecting health (OR=0.11) and did not agree that all vaccines marketed through National Medical Products Administration are safe (OR=0.42) were less willing to be vaccinated. Those who had no one nearby to vaccinate against COVID-19 were less willing to be vaccinated (OR=0.68). The main reasons for refusing or hesitating to be vaccinated were concerned about the safety(73.88%) and efficacy(55.61%) of the vaccine. Further investigation showed that 37.86%, 48.27% and 35.31% of respondents who had previously chosen not to vaccinate or were unsure about vaccinating against COVID-19 were willing to vaccinate if recommended by the government, doctors, relatives and friends, respectively. Conclusion The willingness rate of COVID-19 vaccination among college students was high in Shanghai. The relevant departments should do a good job in the coordination of vaccination so that the vaccination work can be carried out effectively.

7.
Chinese Journal of Disease Control and Prevention ; 26(2):188-192 and 217, 2022.
Article in Chinese | EMBASE | ID: covidwho-1822638

ABSTRACT

Objective To describe the social support, anxiety, and sleep quality of residents in the District of Shanghai during the COVID-19 and to analyze the to correlation of these factors. Methods A structured questionnaire was used to investigate residents' social support, anxiety, and sleep quality. The questionnaire consisted of social support rate scale, the self-rating anxiety scale (SAS) and Pittsburgh sleep quality index (PSQI), investigated the social support, anxiety, and sleep quality of residents in the District of Shanghai under the COVID-19 epidemic and analyzed their potential influencing factors. Structural equation model was constructed to understand the relationship among these factors. Results A total of 258 questionnaires were collected, with 237 being eligible for analyzing. The results showed that there were statistically significant differences in sleep quality (P =0.004) and social support (P =0.009) among residents with different highest education levels. The structural equation model-fitting indices were CFI =0.929, NFI =0.891, IFI =0.930, NNFI =0.907, RMSEA =0.082, χ 2/df =2.599. It indicated that the fitting degree was good. The results showed that the social support of residents could affect their anxiety degree to some extent (r=-0.15). The higher the social support, the lower the anxiety degree they had. Moreover, the degree of anxiety could affect the sleep quality (r =0.72), and the higher the degree of anxiety, the worse the sleep quality they had. Conclusion During the epidemic of COVID-19, residents' social support is related to their anxiety level, and the anxiety level is related to their sleep quality. By improving residents' support, their degree of anxiety could be reduced to improve their sleep quality.

8.
Acm Journal of Data and Information Quality ; 14(2):24, 2022.
Article in English | Web of Science | ID: covidwho-1819938

ABSTRACT

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurantl4, Laptop, Restaurantl6, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on "government" and "lockdown" of 1,658,250 tweets about "#COVID-19" that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users' positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users' emotions over time based on the tweets and on our models.

9.
Frontiers in Earth Science ; 10, 2022.
Article in English | Scopus | ID: covidwho-1809365

ABSTRACT

The Sustainable Development Goals call for taking urgent action to combat climate change and reduce inequalities. However, the related actions have not been effective. Global CO2 emissions in 2021 are projected to rebound to approaching the 2018–2019 peak, and wealth inequality has been increasing at the very top of the distribution resulting from the COVID-19 pandemic. To test whether a trade-off exists between social and environmental benefits, this study calculates county-level wealth inequality with the Gini coefficient and consumption-based household carbon emissions with the emissions coefficient method and input–output modeling. Data are collected from the China Family Panel Studies, the Visible Infrared Imaging Radiometer Suite, the Chinese National Bureau of Statistics and Carbon Emission Account and Datasets in 2014, 2016 and 2018. In addition, a high-dimensional fixed-effects model, an instrumental variable model and causal mediation analysis are adopted to empirically test how wealth inequality influences household carbon emissions and explore the underlying mechanisms. The results show that county-level wealth inequality has a positive impact on household carbon emissions per capita. This means that policies designed to narrow the wealth gap can help reduce carbon emissions, making progress toward multiple SDGs. Moreover, the study reveals that the social norms of the Veblen effect and short-termism play an important role in mediating the relationship between wealth inequality and consumption-based household carbon emissions. This finding provides a new perspective to understand the mechanism behind wealth inequality and household carbon emissions related to climate change. Copyright © 2022 Qin, Wu, Zhang and Wang.

10.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333498

ABSTRACT

OBJECTIVE: The outbreak of novel coronavirus disease 2019 (COVID-19) imposed a substanal health burden in mainland China and remains a global epidemic threat. Our objectives are to assess the case fatality risk (CFR) among CO VID-19 patients detected in mainland China, stratified by clinical category and age group. METHODS: We collected individual information on laboratory-confirmed COVID-19 cases from publicly available official sources from December 29, 2019 to February 23, 2020. We explored the risk factors associated with mortality. We used methods accounting for right-censoring and survival analyses to estimatethe CFR among detected cases. RESULTS: Of 12,863 cases reported outside Hubei, we obtained individual records for 9,651 cases, including 62 deaths and 1,449 discharged cases. The deceased were significantly older than discharged cases (median age: 77 vs 39 years, p<0.001). 58% (36/62) were male. Older age (OR 1.18 per year;95% CI: 1.14 to 1.22), being male (OR 2.02;95% CI: 1.02 to 4.03), and being treated in less developed economic regions (e.g., West and Northeast vs. East, OR 3.93;95 %Cl:1.74 to 8.85) were mortality risk factors. The estimated CFR was 0.89-1.24% among all cases. The fatality risk among critical patients was 2-fold higher than that among severe and critical patients, and 24-fold higher than that among moderate, severe and critical patients. CONCLUSIONS: Our estimates of CFR based on laboratory-confirmed cases ascertained outside of Hubei suggest that COVID-19 is not as severe as severe acute respiratory syndrome and Middle East respiratory syndrome, but more similar to the mortality risk of 2009 H1N1 influenza pandemic in hospitalized patients. The fatality risk of COVID-19 is higher in males and increases with age. Our study improves the severity assessment of the ongoing epidemic and can inform the COVID-19 outbreak response in China and beyond.

11.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333497

ABSTRACT

BACKGROUND: The 2019 novel coronavirus (2019-nCoV or SARS-CoV-2) has spread more rapidly than any other betacoronavirus including SARS-CoV and MERS-CoV. However, the mechanisms responsible for infection and molecular evolution of this virus remained unclear. METHODS: We collected and analyzed 120 genomic sequences of 2019-nCoV including 11 novel genomes from patients in China. Through comprehensive analysis of the available genome sequences of 2019-nCoV strains, we have tracked multiple inheritable SNPs and determined the evolution of 2019-nCoV relative to other coronaviruses. RESULTS: Systematic analysis of 120 genomic sequences of 2019-nCoV revealed co-circulation of two genetic subgroups with distinct SNPs markers, which can be used to trace the 2019-nCoV spreading pathways to different regions and countries. Although 2019-nCoV, human and bat SARS-CoV share high homologous in overall genome structures, they evolved into two distinct groups with different receptor entry specificities through potential recombination in the receptor binding regions. In addition, 2019-nCoV has a unique four amino acid insertion between S1 and S2 domains of the spike protein, which created a potential furin or TMPRSS2 cleavage site. CONCLUSIONS: Our studies provided comprehensive insights into the evolution and spread of the 2019-nCoV. Our results provided evidence suggesting that 2019-nCoV may increase its infectivity through the receptor binding domain recombination and a cleavage site insertion. ONE SENTENCE SUMMARY: Novel 2019-nCoV sequences revealed the evolution and specificity of betacoronavirus with possible mechanisms of enhanced infectivity.

12.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333495

ABSTRACT

BACKGROUND: The COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. METHODS: We collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level. RESULTS: The median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January. CONCLUSION: Our findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

13.
2nd InternationalWorkshop on New Approaches for Multidimensional Signal Processing, NAMSP 2021 ; 270:211-221, 2022.
Article in English | Scopus | ID: covidwho-1797676

ABSTRACT

Now people are facing the pandemic COVID-19 and have to wear masks. This brings a problem in face recognition—occlusion problem and particularly, identifying people wearing masks in 3D-scenes is a great challenge. This study aims to develop a system for tackling this challenge. The 3D-scene is constructed with the 2D-3D coordinate transformation. For the convenience of the fusion between the virtual scene and real scene, a 3D model is achieved by Sketchup Pro. The faces and masks data are explored from the video and occluded faces recognition is achieved with the convolutional neural network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

14.
2021 3rd International Conference on E-Business and E-Commerce Engineering, EBEE 2021 ; : 149-157, 2021.
Article in English | Scopus | ID: covidwho-1789025

ABSTRACT

China is the world's largest consumer and importer of pork. In the context of COVID-19, countries have implemented strict import inspection and quarantine standards, and pork imports are facing more complicated customs clearance procedures, resulting in a sharp increase in customs risks. Pork, as a basic livelihood product, has always been a sensitive topic on the Internet. Their public opinions often have an important impact on customs policies and are one of the important sources of customs risks. Based on Internet text big data mining and LDA-GRA analysis method, this paper classifies online public opinion on pork import during the COVID-19 pandemic into different topics, and conducts correlation analysis on public opinion text and customs policy, investigates the correlation between online public opinion, customs policy and customs risk, as well as its correlation strength. The results show that the online public opinion of pork import has a significant impact on the implementation of the customs policy, and causes a variety of potential customs risks of pork import. Pork import-related enterprises should strengthen public opinion monitoring to reduce losses caused by customs risks. © 2021 ACM.

15.
IEEE Transactions on Intelligent Transportation Systems ; 2022.
Article in English | Scopus | ID: covidwho-1788789

ABSTRACT

The rapid spread of the COVID-19 has not only affected personal health and economy, but also revolutionized people's lifestyles. As more people turn to work and socialize online, the development of unmanned technologies based on the Internet of Vehicles (IoV), such as unmanned delivery, unmanned vehicles, unmanned transportation, etc., will become an inevitable trend. However, all kinds of intelligent terminals for unmanned equipment require a large amount of data interaction with devices such as cloud servers, mobile terminals, and roadside terminals, which poses cyber security risks. Furthermore, the outbreak of COVID-19 has prompted people to put forward higher demands for the security of network communications. Unfortunately, the current intrusion detection methods based on machine learning still have weaknesses such as low accuracy and low efficiency when faced with unbalanced data distribution. To solve the above problems, we propose a novel Tree-based BLS (TBLS) intrusion detection method according to the idea of ensemble learning and decision tree (CART and J48). The performance of TBLS was tested on the NSL-KDD dataset and the UNSW-NB15 dataset respectively, which contain a variety of malicious traffic types for attacks on the IoV. The results show that our proposed method can achieve higher accuracy rate and lower false alarm rate, compared with the existing 16 solutions. IEEE

16.
4th International Conference on Computing and Big Data, ICCBD 2021 ; : 68-74, 2021.
Article in English | Scopus | ID: covidwho-1784901

ABSTRACT

Social media has become an important data resource for knowledge discovery and data mining in multiple disciplines. With the exploding amount of social media data, how to efficiently and effectively exploit values and insights from such overwhelming amount of data has become an emerging area. Recently, various natural language processing techniques have been developed, e.g., word embedding, deep neural network and Latent Dirichlet Allocation (LDA), for studies such as sentiment analysis, traffic event detection, nature disaster assessment and COVID-19 tweet analysis. In this paper, topic modeling through LDA was used to conduct text mining on a large real-world COVID-19 tweet dataset, which contains more than 524 million multilingual tweets and covers 218 countries over a period of 3 months. We conducted extensive experiments and visualise insights discovered through this unsupervised process. © 2021 ACM.

17.
Journal of Men's Health ; 18(3), 2022.
Article in English | EMBASE | ID: covidwho-1780435

ABSTRACT

Background: Children are a vulnerable population in terms of the impact of COVID-19 on their psychological well-being. When restricted to their homes, children are susceptible to problematic Internet gaming (PG). Primary school boys are particularly at risk of PG, which may lead to negative psychological effects, such as distress. Emerging research has identified perceived weight stigma (PWS) as a variable closely associated with both PG and psychological distress, particularly during the COVID-19 pandemic. The purpose of this study was to evaluate the trajectory of psychological distress among this vulnerable population from a longitudinal perspective, evaluating the role of PG and PWS. Methods: Self-report measures were used to assess psychological distress, PG, and PWS among primary school boys (grades 4 to grade 6; N = 283). Data were collected across three waves: before the pandemic, during school closure, and following the lifting of restrictions. Results: The trajectory of psychological distress among primary school boys was concave, indicating their mental health was negatively impacted during home restriction but recovered after the lockdown ended (linear change = 0.98, p < 0.01; quadratic change = -0.19, p < 0.01). PG was a significant covariate in terms of the trajectory of psychological distress (b = 0.02, p < 0.01). Moreover, baseline values for PWS were shown to have a negative direct effect on mental health before the pandemic (b = 0.05, p < 0.01), and moderated the time factor for boys' psychological distress over time (b of PWS × linear change = 0.04, p = 0.006; b of PWS × Quadratic change was negative at -0.01, p = 0.002). Conclusions: Although mental health gradually improved as home restrictions subsided, future studies are required to address changes in mental health upon return to school for students reporting higher levels of weight stigma.

18.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-332200

ABSTRACT

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing towards individuals who are most likely to be infected and, thus, increasing testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6,765 participants) and the MyPHD study (8,580 participants), including smartwatch data from 1,265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate features distinguished between COVID-19 positive and negative cases earlier in the course of the infection than steps features, as early as ten and five days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 3-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve allocation of diagnostic testing resources and reduce the burden of test shortages.

19.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-331977

ABSTRACT

Background: B-cell depleting therapies may lead to protracted disease and prolonged viral shedding in individuals infected with SARS-CoV-2. Viral persistence in the setting of immunosuppression raises concern for viral evolution. Methods: Amplification of sub-genomic transcripts for the E gene (sgE) was done on nasopharyngeal samples over the course of 355 days in a patient infected with SARS-CoV-2 who had previously undergone CAR T cell therapy and had persistently positive SARS-CoV-2 nasopharyngeal swabs. Whole genome sequencing was performed on samples from the patient's original presentation and 10 months later. Results: Over the course of almost a year, the virus accumulated a unique in-frame deletion in the amino-terminal domain of the spike protein, and complete deletion of ORF7b and ORF8, the first report of its kind in an immunocompromised patient. Also, minority variants that were identified in the early samples-reflecting the heterogeneity of the initial infection-were found to be fixed late in the infection. Remdesivir and high-titer convalescent plasma treatment were given, and the infection was eventually cleared after 335 days of infection. Conclusions: The unique viral mutations found in this study highlight the importance of analyzing viral evolution in protracted SARS-CoV-2 infection, especially in immunosuppressed hosts, and the implication of these mutations in the emergence of viral variants. Summary: We report an immunocompromised patient with persistent symptomatic SARS-CoV-2 infection for 335 days. During this time, the virus accumulated a unique in-frame deletion in the spike, and a complete deletion of ORF7b and ORF8 which is the first report of its kind in an immunocompromised patient.

20.
Web of Science; 2021.
Preprint in English | Web of Science | ID: ppcovidwho-331129

ABSTRACT

Background The worldwide surge in coronavirus cases has led to the COVID-19 testing demand surge. Rapid, accurate, and cost-effective COVID-19 screening tests working at a population level are in imperative demand globally. Methods Based on the eye symptoms of COVID-19, we developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The convolutional neural networks (CNNs)-based model was trained on these eye images to complete binary classification task of identifying the COVID-19 cases. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1. The application programming interface was open access. Findings The multicenter study included 2436 pictures corresponding to 657 subjects (155 COVID-19 infection, 23·6%) in development dataset (train and validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19 infections, 13·4%) in test dataset. The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0·913 (95% CI, 0·898-0·927), with a sensitivity of 0·695 (95% CI, 0·643-0·748), a specificity of 0·904 (95% CI, 0·891 -0·919), an accuracy of 0·875(0·861-0·889), and a F1 of 0·611(0·568-0·655). Interpretation The CNN-based model for COVID-19 rapid prescreening has reliable specificity and sensitivity. This system provides a low-cost, fully self-performed, non-invasive, real-time feedback solution for continuous surveillance and large-scale rapid prescreening for COVID-19.

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