[Latent period and incubation period with associated factors of COVID-19 caused by Omicron variant].

Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infections and 335 symptomatic infections in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q1, Q3) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid test within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18-49 years old, the latent period [exp(ß)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(ß)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0-17 years old were prolonged. The latent period [exp(ß)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(ß)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be a influencing factor of the latent period and incubation period.

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM plus Attention

With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

[Epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia Autonomous Region, October 2021].

Objective: To understand the epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia, in October 2021 and provide evidence for the improvement of COVID-19 prevention and control. Methods: The information about the time, area and population distributions of COVID-19 cases in Ejina before November 13, 2021 and the gene sequencing result of the isolates were collected for a statistical descriptive analysis. Results: The first COVID-19 case in Ejina occurred on 7 October, 2021. A total of 164 COVID-19 cases were reported from October 19 to November 12. Most cases were distributed in 6 communities in Darahub (156 cases, 95.12%). The result of full gene sequencing of the isolates indicted that the pathogen was Delta variant (B.1.617.2). The male to female ratio of the cases was 1.3∶1. The age of cases ranged from 1 to 85 years, and the cases aged 20-59 years accounted for 78.66%. The main clinical symptoms were sore throat (91 cases, 91.92%), cough (49 cases, 49.49%) and fever (23 cases, 23.23%). Most cases were ordinary ones (81 cases, 49.39%) and mild ones (68 cases, 41.46%). The cases were mainly detected at the isolation points (84 cases, 51.22%) and through population based nucleic acid testing (62 cases, 37.80%). The basic reproduction number (R0) of COVID-19 was 5.3, the average incubation period was 3.9 days. The local government rapidly started Ⅳ level emergency response and conducted 10 rounds of nucleic acid tests. The transferring of travelers reduced the risk for the further spread of COVID-19 in Ejina. Conclusions: The epidemic of COVID-19 in Ejina characterized by strong transmission, short incubation period, herd susceptibility and case clustering. Delta variant (B.1.617.2) was the pathogen, which might be imported from Zeke port. Comprehensive prevention and control measures, such as closed-loop management and vaccination, should be continued. The successful transferring of the patients and travelers provided evidence for the effective and precise prevention and control of COVID-19 in a routine manner.

[Time distribution of positive nucleic acid detection in imported cases infected with SARS-CoV-2 in China].

Objective: To analyze the time distribution of the first positive nucleic acid detection in imported cases infected with SARS-CoV-2 reported nationwide in China and provide references for further improvement of the prevention and control of COVID-19 in international travelers. Methods: The data of imported cases infected with SARS-CoV-2 reported by provinces from 24 July 2020 and 23 July 2021 were collected for the analysis on the time distribution of the first positive nucleic acid detection after entering China. Results: A total of 7 199 imported cases infected with SARS-CoV-2 were reported in 28 provinces during 24 July 2020 to 23 July 2021. The median interval (Q1, Q3) from the entry to the first positive nucleic acid detection of SARS-CoV-2 was 1 (0, 5) day. The imported cases who had the first positive nucleic acid detections within 14 days and 14 days later after the entry accounted for 95.15% (6 850/7 199) and 4.85% (349/7 199) respectively. Among these cases, 3.65% (263/7 199), 0.88% (63/7 199) and 0.32% (23/7 199) had the first positive nucleic acid detections within 15-21 days, 22-28 days and 28 days later after the entry respectively. The proportion of asymptomatic infections were 47.24% (3 236/6 850) and 63.61% (222/349) among the cases who had the first positive nucleic acid detections within 14 days and 14 days later after the entry respectively. A total of 39.54% (138/349) of cases infected with SARS-CoV-2 with the first positive nucleic acid detections 14 days later after the entry had inter-provincial travel after the discharge of entry point isolation. Conclusions: About 5% of the imported cases infected with SARS-CoV-2 were first positive 14 days later after the entry. In order to effectively reduce the risk of domestic COVID-19 secondary outbreaks caused by imported cases, it is suggested to add a nucleic acid test on 8th -13th day after the entry.

A Characteristic Analysis and Countermeasure Study of the Hedging of Listed Companies in China Stock Markets

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

[Distribution and infectious characteristics of re-positive cases infected with SARS-CoV-2].

Domestic and foreign literatures related to the persistence of SARS-CoV-2 and the re-positive cases infected with SARS-CoV-2 were reviewed, and the characteristics and infectivity of the re-positive cases were analyzed to provide scientific evidence for the improvement of case management and the development of measures to stop the spread of SARS-CoV-2. Existing studies have shown that re-positive rate of SARS-CoV-2 ranged from 2.4% to 19.8%, the median of interval between re-positive detection and discharge was 4-15 days. Following the second course of the disease, the anti-SARS-CoV-2 IgM, IgG and IgA positive rates of the cases were 11.11%-86.08%, 52.00%-100.00% and 61.54%-100.00% respectively, the total antibody and neutralizing antibody positive rates were 98.72% and 88.46%. The viral load of the re-positive cases was lower than that in the initial infection. At least 3 380 re-positive cases have been reported globally. SARS-CoV-2 strains were isolated from the samples of 3 re-positive cases (1 immunodeficiency case and 2 cases with abnormal pulmonary imaging). There were close contacts that were infected by an asymptomatic case taking immunosuppressive agents. In conclusion, the infectivity of re-positive cases infected with SARS-CoV-2 is generally very low. Rare re-positive cases infected with SARS-CoV-2 might cause further transmission. The management approach for the re-positive cases can be based on the assessment of the individual transmission risk according to the pathogen detection results.