Epidemiological, clinical, and household transmission characteristics of children and adolescents infected with SARS-CoV-2 Omicron variant in Shanghai, China: a retrospective, multicenter observational study.

OBJECTIVES: To describe the epidemiological, clinical, and household transmission characteristics of pediatric COVID-19 cases in Shanghai, China. METHODS: Pediatric patients with COVID-19 hospitalized in Shanghai from March-May 2022 were enrolled in this retrospective, multicenter cohort study. The symptoms and the risk factors associated with disease severity were analyzed. RESULTS: In total, 2620 cases (age range, 24 days-17 years) were enrolled in this study. Of these, 1011 (38.6%) were asymptomatic, whereas 1415 (54.0%), 190 (7.3%), and 4 (0.2%) patients developed mild, moderate, and severe illnesses, respectively. Household infection rate was negatively correlated with household vaccination coverage. Children aged 0-3 years, those who are unvaccinated, those with underlying diseases, and overweight/obese children had a higher risk of developing moderate to severe disease than children aged 12-17 years, those who were vaccinated, those without any underlying disease, and those with normal weight, respectively (all P <0.05). A prolonged duration of viral shedding was associated with disease severity, presence of underlying diseases, vaccination status, and younger age (all P <0.05). CONCLUSION: Children aged younger than 3 years who were not eligible for vaccination had a high risk of developing moderate to severe COVID-19 with a prolonged duration of viral shedding. Vaccination could protect children from COVID-19 at the household level.

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise’s performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-SVM model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input–output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.