Unfavorable progression of obesity in children and adolescents due to COVID-19 pandemic: A school-based survey in China.

OBJECTIVE: The Chinese government decisively imposed nationwide confinement in response to the COVID-19 outbreak. This study aimed to evaluate the impact of the COVID-19 pandemic on the progression of obesity in children and adolescents in Changshu, China. METHODS: Based on the Health Promotion Program for Children and Adolescents (HPPCA), which is a prospective cross-sectional and school-based study, BMI assessed in seven consecutive years (2014 to 2020) among children and adolescents aged 6 to 17 years in Changshu city was extracted. The standardized BMI z scores (zBMI) and prevalence of obesity between 2020 (after COVID-19 home confinement) and the previous 6 years were compared among age-specific subgroups and between sexes. RESULTS: The mean number of participants per year was 29,648. The overall mean zBMI drastically increased from 0.29 in 2019 to 0.45 in 2020, resulting in a rise of 0.16 (95% CI: 0.14-0.18); the prevalence of obesity substantially elevated to 12.77% in 2020 (versus 10.38% in 2017), with an acceleration of 2.39% (95% CI: 1.88%-2.90%). Of note, these increases were more likely to be observed in boys and those 6 to 11 years old. CONCLUSIONS: The COVID-19 pandemic seemed to exacerbate the obesity epidemic among pediatric populations in Changshu, China.

Modeling and prediction of the transmission dynamics of COVID-19 based on the SINDy-LM method.

The transmission dynamics of COVID-19 is investigated in this study. A SINDy-LM modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, the Sparse Identification of Nonlinear Dynamical systems (SINDy) method is used to discover and describe the nonlinear functional relationship between the dynamic terms in the model in accordance with the observation data of the COVID-19 epidemic. Moreover, the Levenberg-Marquardt (LM) algorithm is utilized to optimize the obtained model for improving the accuracy of the SINDy algorithm. Second, the obtained model, which is consistent with the logistic model in mathematical form with small errors and high robustness, is leveraged to review the epidemic situation in China. Otherwise, the evolution of the epidemic in Australia and Egypt is predicted, which demonstrates that this method has universality for constructing the global COVID-19 model. The proposed model is also compared with the extreme learning machine (ELM), which shows that the prediction accuracy of the SINDy-LM method outperforms that of the ELM method and the generated model has higher sparsity.