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1.
Zhonghua Yu Fang Yi Xue Za Zhi ; 56(7): 912-918, 2022 Jul 06.
Article in Chinese | MEDLINE | ID: covidwho-1964140

ABSTRACT

Objective: To understand the common viral infection among the surveillance cases of fever respiratory syndrome (FRS) in nine provinces in China. Methods: The research data were obtained from nine provinces (Anhui, Beijing, Guangdong, Hebei, Hunan, Jilin, Shandong, Shaanxi and Xinjiang) in the "Infectious Disease Surveillance Technology Platform Information Management System" of the Chinese Center for Disease Control and Prevention from January 2009 to June 2021. Finally, 8 243 FRS cases with nucleic acid detection results of eight viruses [human influenza virus (HIFV), human respiratory syncytial virus (HRSV), human adenovirus (HAdV), human parainfluenza virus (HPIV), human rhinovirus (HRV), human metapneumovirus (HMPV), human coronavirus (HCoV) and human Boca virus (HBoV)] were included in the study. The χ2 test/Fisher exact probability method was used to analyze the difference of virus detection rate in different age groups, regions and seasons. Results The M (Q1, Q3) age of 8 243 FRS cases was 4 (1, 18) years old, and 56.56% (4 662 cases) were children under 5 years old. Males accounted for 58.1% (4 792 cases) of all cases. All cases were from outpatient/emergency department (2 043 cases) and inpatient department (6 200 cases). The virus detection rates of FRS cases from high to low were HRSV, HIFV, HPIV, HRV, HAdV, HMPV, HCoV and HBoV. Two or more viruses were detected simultaneously in 524 cases, accounting for 15.66% of virus-positive cases. The difference of the virus detection rate in different age groups was statistically significant (all P values<0.05), and the virus detection rate in children<5 years old was higher (49.96%). The positive rate of any virus in south China was higher than that in north China (P<0.001). The virus-positive FRS cases were detected throughout the year. The detection rate of HRSV was higher in autumn and winter. The detection rate of HIFV was higher in winter. The detection rate of HMPV was higher in winter and spring. The detection rates of HPIV, HRV, HCoV and HBoV were higher in summer and autumn, while there was no significant difference in the detection rate of HAdV in different seasons. Compared with 2009-2019, the detection rate of any virus in 2020-2021 decreased from 41.37% to 37.86%. The detection rate of HIFV decreased sharply from 10.62% to 1.37%. The detection rate of HPIV decreased from 8.24% to 5.88%. The detection rate of HRV and HBoV increased from 5.43% and 1.79% to 9.67% and 3.19%, respectively. Conclusion: HRSV and HIFV infections are more common among FRS cases in nine provinces in China from 2009 to 2021, and the epidemiological characteristics of eight common respiratory viruses vary in different age groups, regions and seasons.


Subject(s)
Orthomyxoviridae , Respiratory Syncytial Virus, Human , Respiratory Tract Infections , Virus Diseases , Viruses , Child , Child, Preschool , China/epidemiology , Humans , Infant , Male , Respiratory System , Respiratory Tract Infections/epidemiology , Virus Diseases/epidemiology
2.
Somatechnics ; 12(1-2):92-103, 2022.
Article in English | Scopus | ID: covidwho-1963389

ABSTRACT

This article draws on the paradigm of media operationalism to understand the somatechnical construction of bodies during the COVID-19 pandemic. According to the concept of somatechnics, one’s experience with the social world is articulated through the available technologies and techniques required to and developed from using these technologies (Sullivan and Murray 2016). By drawing on the case of the Service Victoria app, the digital COVID-19 contact tracing system launched by the Victoria State government in Australia, I focus on the transformative meaning of technologies and somatechnics and how subjectivity is being redefined through the lens of technological utilisation. I suggest that all human-related forms of relations (human-to-human and human-to-machine) have become secondary and give way to the synchronic data-to-data relation of the app. In the regime of operational media, the body is not just a historical and cultural construction but a techno-transactional object that supports the optimisation of automated-decision making. The recent operational-turn in media studies provides a useful pathway to rethink the changing meaning of body and the human/technologies entanglement. © Edinburgh University Press.

3.
International Journal of Gerontology ; 16(2):89-94, 2022.
Article in English | EMBASE | ID: covidwho-1957563

ABSTRACT

Background: Our study evaluates the efficacy of an outpatient personalized multidisciplinary intervention model guided by comprehensive geriatric assessment (CGA), for pre-frail and frail elderly. Methods: A single-arm self-controlled study was conducted at the outpatient departments (OPD) of a medical center in Taiwan. Subjects received personalized multidisciplinary intervention, including physical therapy, psychotherapy, a nutritional consultation, precise medicine, and social resource linkage, as determined by the results of their CGAs. After 3 months of interventions, change in the proportions of the frail status (frail, pre-frail and robust), functional scores, depressive status, cognition, nutritional status, percentage of inappropriate medication used and social resource usage were analyzed. A logistic regression model was applied to determine the predictive factors associated with an improvement in frail severity. Results: A significant improvement in frail status was found (proportion of frail: 44.5% versus 23.1%, p < 0.001). Physical function, depressive and nutritional status were also significantly improved. 18.5% of participants used inappropriate medications, with benzodiazepine hypnotics the most common (40.9%). 24.2% of subjects were successfully linked to social resources. The presence of the frail phenotypes exhaustion was significantly associated with an improvement in frail severity (odds ratio (OR) = 2.77, 95% confidence interval (CI) = 1.15–6.66, p = 0.023). There was a significant dose response relationship between the improvement of frail status and physical training times (proportion of improved frail status: 23.7%, 40.5% and 47.9% for 0, 1–3, and 4–6 times of physical training, p = 0.03). Conclusion: The reported CGA-based, personalized multidisciplinary intervention model was effective at improving frail severity among pre-frail and frail elderly in OPDs.

4.
FRONTIERS IN EDUCATION ; 7, 2022.
Article in English | Web of Science | ID: covidwho-1938611

ABSTRACT

Since the coronavirus disease 2019 (COVID-19) pandemic, human parasitology education has been exceedingly disrupted. To deliver human parasitology knowledge, medical universities in China have employed multiple measures, some of which have had positive outcomes that have not yet been summarized. The objective of this review is to share the Chinese experience as the human parasitology teaching methods were transformed. In general, we adopted a fully online teaching model under urgent pandemic control measures based on a well-structured teaching model that integrated the course preview, live lecture, review, and assessment. Combinations were attempted of COVID-19 and parasitology teaching contents. Some active learning models, such as case-based e-learning and flipped classrooms, were proposed for offline and online blended teaching during the normalization stage of the pandemic. Meanwhile, we discuss both the strengths and flaws of online and blended teaching. Some useful assessment tools are presented for reference purposes. In conclusion, this transition to online and online-offline blended human parasitology teaching in China has boosted innovative teaching activities and may continue to catalyze the transformation of medical education.

5.
2nd International Conference on IoT and Big Data Technologies for HealthCare, IoTCare 2021 ; 415 LNICST:488-496, 2022.
Article in English | Scopus | ID: covidwho-1930262

ABSTRACT

The continued and rapid global spread of COVID-19 is taking a heavy toll on the global economy and human health, which has attracted the attention of professionals in various fields. Controlling the spread of this disease and reducing the threat to human life is of paramount importance. There are no clinically effective drugs for this disease. However, research on deep learning-based diagnostic systems for COVID-19 has yielded significant results and is expected to be an essential weapon in the fight against COVID-19 in the future. This paper provides a brief summary and evaluation of 15 studies on deep learning-based COVID-19 diagnostics, covering a total of 13 common pre-trained models and nine custom deep learning models in the COVID-19 dataset, and discusses the current challenges and future trends in this category of research. This paper aims to help healthcare professionals and researchers understand the advances in deep learning techniques for COVID-19 diagnosis to assist them in conducting relevant research to stop the further spread of COVID-19. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

6.
2nd International Conference on IoT and Big Data Technologies for HealthCare, IoTCare 2021 ; 415 LNICST:479-487, 2022.
Article in English | Scopus | ID: covidwho-1930261

ABSTRACT

The rapid global spread of COVID-19 poses a huge threat to human security. Accurate and rapid diagnosis is essential to contain COVID-19, and an artificial intelligence-based classification model is an ideal solution to this problem. In this paper, we propose a method based on wavelet entropy and Cat Swarm Optimization to classify chest CT images for the diagnosis of COVID-19 and achieve the best performance among similar methods. The mean and standard deviation of sensitivity is 74.93 ± 2.12, specificity is 77.57 ± 2.25, precision is 76.99 ± 1.79, accuracy is 76.25 ± 1.49, F1-score is 75.93 ± 1.53, Matthews correlation coefficient is 52.54 ± 2.97, Feature Mutual Information is 75.94 ± 1.53. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

7.
Sleep ; 45(SUPPL 1):A95-A96, 2022.
Article in English | EMBASE | ID: covidwho-1927394

ABSTRACT

Introduction: Circadian rhythms have critical roles in human health. We quantified the effect of time-of-day of COVID-19 vaccination and other covariates on self-reported side effects post vaccination. Methods: The dataset was created from MassGeneralBrigham (MGB) electronic health records and REDCap survey that collected self-reported symptoms for 1-3 days after each immunization. Variables are demographics (age, sex, race, and ethnicity), vaccine manufacturer, clock time of vaccine administration/appointment, any COVID-19 diagnosis/positive test prior to vaccination, any history of allergy, and any note of epinephrine self-injection (e.g., EpiPen) medication. Time of day groupings were morning (6 am10 am), midday (10 am2 pm), late afternoon (2 pm6 pm) or evening (6 pm10 pm). Side effects were classified as Allergic (Rash;Hives;Swollen lips, tongue, eyes, or face;Wheezing) and Non-Allergic (New Headache, New Fatigue, Arthralgias, Myalgias, Fever) symptoms. The study was approved by the MGB IRB.Machine learning (ML) techniques (e.g., extreme gradient boosting) were applied to the variables to predict the occurrence of side effects. Stratified k-fold cross validation was used to validate the performance of the ML models. Shapley Additive Explanation values were computed to explain the contribution of each of the variables to the prediction of the occurrence of side effects. Results: Data were from 54,844 individuals. On day 1 after the first vaccination, (i) females, people who received the Moderna vaccine, and those with any allergy history were more likely to report Allergic side effects;and (ii) females, people who received the Janssen vaccine, those who had prior COVID-19 diagnosis ,and those who received their vaccine in the morning or midday and were more likely to report Non-Allergic symptoms. Older persons had fewer side effects of any type. Conclusion: ML techniques identified demographic and time-ofday- of-vaccination effects on side effects reported on the first day after the first dose of a COVID-19 vaccination. We will use these techniques to test for changes on days 2 and 3 after the first dose, and the first 3 days after the second dose and for the influence of recent night or shiftwork. Future work should target underlying physiological reasons.

8.
Chinese Journal of Disease Control and Prevention ; 26(6):703-708, 2022.
Article in Chinese | EMBASE | ID: covidwho-1928936

ABSTRACT

Objective To evaluate post-traumatic stress disorder (PTSD) prevalence alter the outbreak of COVID-19 in Chinese adults and to explore the vulnerable groups of COVID-19-associated PTSD. Methods Random digital dialling sampling method was used based on the national cell phone segment database. Computer assisted telephone interviewing platform w, as administered to collect 4 206 questionnaires from all prefecture-level cities in China from May 11th to 28th 2020. PTSD score was evaluated with the post-traumatic stress disorder checklist 5th edition (PCL-5). Results The total national suspected PTSD rate was 2. 06%. The suspected PTSD rate for Wuhan City, other areas in Hubei and other Provinces were 2. 88%, 2. 54% and 2. 03% respectively. The multivariate Logistic regression model showed that middle age, rural location, and lower social-economic status was positively associated with suspected PTSD. Conclusions Although the COVID-19-associated PTSD rate was relatively low, more attention should be paid because of the largest population in China. And more consideration should be given to the low SES population for the control and prevention of COVID-19-associated PTSD.

9.
Global Advances in Health and Medicine ; 11:64, 2022.
Article in English | EMBASE | ID: covidwho-1916560

ABSTRACT

Methods: A three-arm, pilot, randomized controlled, mixed-methods clinical trial design was used to randomize cancer caregivers to: 1) virtual Qigong classes;2) in-person Qigong classes;or 3) a self-care control. Feasibility goals included recruiting 54 caregivers over 12 months, ≥ 50% of screened individuals study eligible, ≥ 50% of eligible individuals enrolled, and < 20% lost to follow-up at 12 weeks. Participants were considered adherent to the intervention if they attended ≥ 70% of all Qigong classes. Results: A total of 47 caregivers were recruited (in-person group: n=15;virtual group: n=16;control group: n=16), thus falling short of the recruitment goal by 13%. All other feasibility metrics were met: 1) out of total individuals screened, 72% were eligible;2) 64% of those eligible enrolled in the study;3) 13% were lost to follow-up;and 4) 63% and 73% of participants in the virtual group and in person group attended ≥ 70% of all Qigong classes, respectively. Background: Caregiving for someone with cancer can cause significant psychological and physical distress, leading to lower overall quality of life. Although mind-body interventions offer a solution for caregiver distress and to improve quality of life, current research has not evaluated the virtual delivery of mind-body programs for caregivers in the home. The purpose of this study was to examine and compare the feasibility of providing a virtual Qigong program, an in-person Qigong program, and a self-care control for cancer caregivers. Conclusion: Findings indicate that a virtual Qigong intervention for cancer caregivers is feasible. Not meeting the recruitment goal was partially explained by the COVID-19 pandemic occurring during the study time period. Ongoing analyses of qualitative and quantitative data will inform facilitators and barriers related to meeting the feasibility metrics, as well as providing initial data regarding the effectiveness of Qigong programs for subsequent clinical trials.

10.
Studies in Mycology ; 101:417-564, 2022.
Article in English | CAB Abstracts | ID: covidwho-1902874

ABSTRACT

This paper is the fourth contribution in the Genera of Phytopathogenic Fungi (GOPHY) series. The series provides morphological descriptions and information about the pathology, distribution, hosts and disease symptoms, as well as DNA barcodes for the taxa covered. Moreover, 12 whole-genome sequences for the type or new species in the treated genera are provided. The fourth paper in the GOPHY series covers 19 genera of phytopathogenic fungi and their relatives, including Ascochyta, Cadophora, Celoporthe, Cercospora, Coleophoma, Cytospora, Dendrostoma, Didymella, Endothia, Heterophaeomoniella, Leptosphaerulina, Melampsora, Nigrospora, Pezicula, Phaeomoniella, Pseudocercospora, Pteridopassalora, Zymoseptoria, and one genus of oomycetes, Phytophthora. This study includes two new genera, 30 new species, five new combinations, and 43 typifications of older names.

11.
2021 International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2021 ; 12163, 2022.
Article in English | Scopus | ID: covidwho-1901895

ABSTRACT

At the beginning of 2020, COVID-19 broke out in Wuhan and quickly swept the world. At present, the global epidemic prevention and control is still facing severe challenges. Scientific and effective measures of the epidemic is crucial to epidemic prevention and control. In this paper, a COVID-19 diffusion prediction model is established based on the impulsive partial differential equation and traditional infectious disease model, which can describe the spatial diffusion of viruses. This is also a lack of other models. The model divides the total population into seven groups: susceptible, quarantine, exposed, asymptomatic, infected, diagnosed and recovered, while considering the influence of time and space on the spread of the virus. In order to test the model, we take Jiangsu Province in China as an example, compare the calculated results with the actual data, and verify the effectiveness of the model through numerical calculation. © COPYRIGHT SPIE.

12.
9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022 ; 13258 LNCS:125-135, 2022.
Article in English | Scopus | ID: covidwho-1899008

ABSTRACT

The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner. The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models. © 2022, Springer Nature Switzerland AG.

13.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi ; 34(2): 172-178, 2022 Apr 13.
Article in Chinese | MEDLINE | ID: covidwho-1893445

ABSTRACT

OBJECTIVE: To investigate the health-seeking behaviors of imported malaria cases after returning to China, and to investigate the factors affecting the time to initial diagnosis, so as to provide the scientific evidence for early identification of imported malaria cases and prevention of severe cases development and secondary transmission. METHODS: The individual demographic features, and the disease onset and the time to initial diagnosis of imported malaria cases in Jiangsu Province in 2019 were captured from the National Notifiable Disease Report System and the Information Management System for Parasitic Disease Control in China. The characteristics of health-seeking behaviors and epidemiological features of imported malaria cases were descriptively analyzed, and the factors affecting the time to initial diagnosis of imported malaria cases after returning to China were identified using multivariate logistic regression analysis. RESULTS: A total of 244 imported malaria cases were reported in Jiangsu Province in 2019, and the time to initial diagnosis of the cases were 1-12 days, with mean time of (1.53 ± 1.65) days, with median time of one day. The highest number of malaria cases seeking healthcare services were found on the day of developing primary symptoms (76 cases, 31.1%), followed by on the second day (68 cases, 27.9%), on the third day (46 cases, 18.9%), and 54 cases (22.1%) received initial diagnosis 3 days following presence of primary symptoms, including 3 cases with initial diagnosis at more than one week. High proportions of imported malaria cases with a delay in the time to initial diagnosis were seen in migrant workers who returned to China in January (14 cases, 5.7%) and December (13 cases, 5.3%) and those aged between 41 and 50 years (32 cases, 13.1%). Multivariate logistic regression analysis showed relative short time to initial diagnosis among imported malaria cases returning to China on March [odds ratio (OR) = 0.16, P = 0.03, 95% confidence interval (CI): (0.03, 0.85)] and those with a history of overseas malaria parasite infections [OR = 0.36, P = 0.001, 95% CI: (0.19, 0.67)]. CONCLUSIONS: Timely health-seeking behaviors should be improved among imported malaria cases in Jiangsu Province, patients with a history of overseas malaria infections require faster health-seeking activities.


Subject(s)
Malaria , Transients and Migrants , Adult , China/epidemiology , Humans , Malaria/diagnosis , Malaria/epidemiology , Malaria/parasitology , Middle Aged
14.
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.

15.
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.

16.
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.

17.
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.

18.
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

19.
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.

20.
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.

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