Intelligent Campus System Design Based on Digital Twin

Amid the COVID-19 pandemic, prevention and control measures became normalized, prompting the development of campuses from digital to intelligent, eventually evolving to become wise. Current cutting-edge technologies include big data, Internet of Things, cloud computing, and artificial intelligence drive campus innovation, but there are still problems of unintuitive scenes, lagging monitoring information, untimely processing, and high operation and maintenance costs. Based on this, this study proposes the use of digital twin technology to digitally construct the physical campus scene, fully digitally represent it, accurately map the physical campus to the virtual campus with real-time sensing, and remotely control it to achieve the reverse control of the twin virtual campus to the physical campus. The research is guided by the theoretical model proposed by the digital twin technology, using UAV tilt photography and 3D modelling to collaboratively build the virtual campus scene. At the design stage, the interactive channel of the system is developed based on Unity3D to the realize real-time monitoring, decision making and prevention of dual spatial data. A design scheme of the spiral optimization system life cycle is formed. The modules of the smart campus system were evaluated using a system usability scale based on student experience. The experimental results show that the virtual-real campus system can enhance school management and teaching, providing important implications for promoting the development and application of campus intelligent systems.

A Dynamic Interaction Semiparametric Function-on-Scalar Model

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects. We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions. In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic. The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies. Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data. In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations. For instance, we find that the interaction effect of the population ageing and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1,000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic. The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model. [ABSTRACT FROM AUTHOR] Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Working conditions and health status of 6,317 front line public health workers across five provinces in China during the COVID-19 epidemic: a cross-sectional study (preprint)

Background: Public health workers at the Chinese Centre for Disease Control and Prevention (China CDC) and primary health care institutes (PHIs) were among the main workers who implemented prevention, control, and containment measures. However, their efforts and health status have not been well documented. We aimed to investigate the working conditions and health status of front line public health workers in China during the COVID-19 epidemic. Methods: : Between 18 February and 1 March 2020, we conducted an online cross-sectional survey of 2,313 CDC workers and 4,004 PHI workers in five provinces across China experiencing different scales of COVID-19 epidemic. We surveyed all participants about their work conditions, roles, burdens, perceptions, mental health, and self-rated health using a self-constructed questionnaire and standardised measurements (i.e., Patient Health Questionnaire and General Anxiety Disorder scale). To examine the independent associations between working conditions and health outcomes, we used multivariate regression models controlling for potential confounders. Results: : The prevalence of depression, anxiety, and poor self-rated health was 21.3%, 19.0%, and 9.8%, respectively, among public health workers (27.1%, 20.6%, and 15.0% among CDC workers and 17.5%, 17.9%, and 6.8% among PHI workers). The majority (71.6%) made immense efforts in both field and non-field work. Nearly 20.0% have worked all night for more than 3 days, and 45.3% had worked throughout the Chinese New Year holiday. Three risk factors and two protective factors were found to be independently associated with all three health outcomes in our final multivariate models: working all night for >3 days (multivariate odds ratio [ORm]=1.67~1.75, p <0.001), concerns about infection at work (ORm=1.46~1.89, p <0.001), perceived troubles at work (ORm=1.10~1.28, p <0.001), initiating COVID-19 prevention work after January 23 (ORm=0.78~0.82, p =0.002~0.008), and ability to persist for > 1 month at the current work intensity (ORm=0.44~0.55, p <0.001). Conclusions: : Chinese public health workers made immense efforts and personal sacrifices to control the COVID-19 epidemic and faced the risk of mental health problems. Efforts are needed to improve the working conditions and health status of public health workers and thus maintain their morale and effectiveness during the fight against COVID-19.

Difficulties of Prevention and Control Staff in COVID-19 Outbreak: A Cross-sectional Study Based on Five Provinces (preprint)

Objectives: The aim of this study is to address the difficulties encountered by epidemic control staff in the early and middle stages of their efforts to combat COVID-19, compare the gaps among different types of institutions, and identify shortcomings in epidemic control. Methods: Using multi-stage sampling, a survey of primary (“primary-urban” and “primary-rural”) and non-primary (“CDC”) public health workers involved in the prevention and control of COVID-19 in five provinces, including Hubei, Guangdong, Sichuan, Jiangsu and Gansu, was conducted from 18 February to 1 March 2020 through a self-administered questionnaire.Results: A total of 9475 outbreak prevention and control workers were surveyed, of which 40.0% were from the primary-rural, 27.0% were from the primary-urban and 33.0% were CDC. Resources shortage was reported at 27.9%, with the primary-rural being the worst affected (OR=1.201, 95%CI: 1.073-1.345). Difficulties in data processing were reported at 31.5%, with no significant differences among institutions. Communication and coordination difficulties were reported at 29.8%, with the CDC being the most serious (the rural primary: OR=0.520, 95%CI: 0.446-0.606; the primary-urban: OR=0.533, 95%CI: 0.454-0.625). Work object difficulties were reported at 20.2%, with the primary-urban being the worst (OR=1.368, 95%CI: 1.199-1.560). Psychological distress was reported at 48.8%, with no significant differences among institutions.Conclusions: Psychological distress is the most serious problem in the prevention and control of COVID-19, and the resources shortage in primary-rural, communication and coordination difficulties in CDC, and difficulties in working with the target population in the primary-urban deserve attention. This study will provide a scientific basis for improving the national public health emergency management system, especially for reducing the urban-rural differences in emergency response capacity.