ABSTRACT
China's aging population has deeply affected the sustainable development of the Chinese economy. Based on the provincial panel data of China's population and economic indicators from 2000 to 2020, this paper develops a panel vector autoregressive model to analyze the effect of China's aging population on economic growth under the paths of household consumption and national savings, respectively. The results show that an aging population inhibits household consumption and promotes national saving, which has both direct and indirect effects on economic growth. In particular, an aging population is not conducive to sustainable economic development in the context of China's slow population growth over the past three years and the contraction of the global economy due to the COVID-19 pandemic. In accordance with the empirical results, this paper puts forward corresponding policy recommendations, as follows: improve the pension security system;develop the silver-hair industry;expand domestic demand in China;encourage fertility;and increase human capital investment to provide an impetus for sustainable economic development.
ABSTRACT
This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.
ABSTRACT
The prevention and control of COVID-19 epidemic is a great challenge for human beings today. In the battle against COVID-19, the hierarchical treatment measures based on symptom classifications have proved to be a particularly effective way to deal with the large-scale epidemic in the absence of adequate medical resources. This paper deals with the epidemic dynamic analyses of the COVID-19 based on a modified SEIR model with different symptoms. First, by taking symptom classifications and hierarchical treatments of patients into account, a modified SEIR model is established. Then, the proposed differential equations model is solved by using Runge-Kutta methods, and the parameters herein are estimated by least square principle based on the data released by the National Health Commission. Simulation results of the model show that the introduction of symptom classifications in the SEIR model can not only improve the fitting accuracy, but also precisely describe the evolution rules and mutual transfer rules of patients with different symptoms. The model can provide theoretical support for decision-making of the corresponding government departments, especially for the construction of mobile cabin hospitals and the reasonable preparation of important epidemic prevention resources. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.