Partially Linear Additive Functional Regression

We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concernedwith from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic.

[Clinical characteristics and outcome analysis of COVID-19 patients with pulmonary tuberculosis].

Coronavirus disease (COVID-19) and tuberculosis (TB) are two respiratory infectious diseases with a high incidence of transmission, mainly via respiratory droplets and both can weaken the immune system and lower the number of CD4+T cells in patients. COVID-19 can occur before, at the same time or after the diagnosis of TB. Patients with pulmonary TB are more likely to have co-infection when they have a history of epidemiological exposure to COVID-19. At present, many cases of nosocomial infection of COVID-19 caused by ineffective prevention and control measures in tuberculosis hospitals have been reported successively at domestic and overseas. Therefore, it is urgent to strengthen the prevention and control of nosocomial infections in tuberculosis hospitals. The superposition of the two diseases can lead to a worsening prognosis, aggravating the patient's condition and making treatment more difficult. In addition, in the context of the new coronavirus epidemic, early recognition of co-infection with new coronavirus should be made when TB patients in chest hospitals present with symptoms such as aggregated fever or progressive disease. At the same time, we should focus on identifying the clinical and imaging manifestations of TB and COVID-19 co-infection. At present, research on COVID-19 complicated with pulmonary TB is scarce, and there are disputes on many aspects. As a country with a high prevalence of tuberculosis, it is of great practical significance to identify the clinical characteristics, outcomes, and treatment of the two infectious diseases in China.

Smart Robotic System to Fight the Spread of COVID-19 at Construction Sites

To prevent the spread of the COVID-19 virus at construction sites, accounting for the surveillance and precautions imposed by the pandemic means frequent contact tracing, symptom monitoring, and PPE reminders. During the pandemic, many construction projects continued because of the need for a priority building that was under construction, such as for a healthcare project. To promote safety under unprecedented circumstances, this paper proposes an intelligent robotic surveillance system that can locate workers and identify whether they are properly wearing masks. The system leverages autonomous terrestrial robots equipped with wide-angle cameras to capture images of their surroundings and a two-dimensional laser scanner for simultaneous localization and mapping (SLAM). In this system, the robot is equipped with precise image processing in a well-trained convolutional neural network (VGG16) to recognize workplace entities, particularly workers and their masks, with 83.3% accuracy. Simultaneously, the mounted laser scanner enables the robot to generate the map of the surrounding environment based on the near-real-time Hector SLAM algorithm. In situ recognition would help track workers who improperly use masks and trigger interventions that would diminish the spread of the virus at the construction site. The proposed robotic system will non-intrusively and privately inform workers to use proper protocol to protect them from COVID-19 and other deadly viruses, thereby improving health and safety.

[Interpretation for the group standards in the handling of accidental leak coronavirus disease 2019 sample].

Biosafety is an important guarantee of the new coronavirus laboratory test. The accident treatment of sample overflow and sprinkle is a necessary part of the emergency plan for testing activities. Beijing Preventive Medicine Association coordinated biosafety experts of COVID-19 laboratories from Beijing CDC, to write up "The standard for handling of accidents of corona virus disease 2019 sample (T/BPMA 0005-2020)" . The group standard was based on the guidelines of China and WHO, and combined with the practical experience of COVID-19 epidemic and the principle of "scientific, normative, applicable and feasible" . Through all kinds of risk Assessment, it included the spillover of samples caused by the packing of COVID-19 (highly pathogenic) samples, the overflow and sprinkle in the laboratory during the detection operation, and the spillage accident occurred during the transfer of samples in the same building. The standard could guide and standardize the handling methods of accidental overflow and sprinkle that may occur in the SARS-CoV-2 testing laboratories in the city.

[Typical case analysis of COVID-19 cluster epidemic in Shaanxi, 2020].

Objective: By analyzed the transmission patterns of 4 out of the 51 COVID-19 cluster cases in Shaanxi province to provide evidences for the COVID-19 control and prevention. Methods: The epidemiological data of RT-PCR test-confirmed COVID-19 cases were collected. Transmission chain was drawn and the transmission process was analyzed. Results: Cluster case 1 contained 13 cases and was caused by a family of 5 who traveled by car to Wuhan and returned to Shaanxi. Cluster case 2 had 5cases and caused by initial patient who participated family get-together right after back from Wuhan while under incubation period. Cluster case 3 contained 10 cases and could be defined as nosocomial infection. Cluster case 4 contained 4 cases and occurred in work place. Conclusion: Higher contact frequency and smaller places were more likely to cause a small-scale COVID-19 cluster outbreak, with potential longer incubation period. COVID-19 control strategies should turn the attention to infection prevention and control in crowded places, management of enterprise resumption and prevention of nosocomial infection.

[Epidemiological characteristics of COVID-19 in Shaanxi province].

Objective: To understand the incidence trend and epidemiological characteristics of COVID-19 in Shaanxi province. Methods: The incidence data of COVID-19 reported in Shaanxi as of 22 February, 2020 were collected for an epidemiological descriptive analysis. Results: A total of 245 confirmed cases of COVID-19 were reported in Shaanxi. Most cases were mild (87.76%). As time passed, the areas where confirmed cases were reported continued to increase. The case number in Xi'an was highest, accounting for nearly half of the total reported cases in the province. The epidemic pattern in Shaanxi had gradually shifted from imported case pattern to local case pattern, and the transmission of local cases was mainly based on family cluster transmission. The confirmed cases from different sources had caused the secondary transmission in Shaanxi. After February 7, the number of reported cases began to fluctuate and decrease stably, indicating a decrease-to-zero period. Conclusions: At present, the overall epidemic of COVID-19 in Shaanxi has gradually been mitigated. However, considering the approaching of return to work and study and the increasing of imported cases from other countries, the prevention and control of COVIS-19 in Shaanxi will face new challenges.