The Effectiveness of China's Fiscal and Monetary Policy and the Impact of Uncertainty Shocks: From the Perspective of Three Industries (preprint)

Under the influence of major random events such as US-China trade dispute and the coronavirus (COVID-19) outbreak, the uncertainty of macroeconomic regulation has also risen significantly. Then, what role economic policy level shocks and uncertainty shocks play in industrial regulation need to be clarified urgently. This study constructs a large-scale benchmark VAR model including fiscal and monetary policy and three-industry output and inflation, makes a detailed distinction between policy level shocks and uncertainty shocks, and describes the industrial control mechanism of economic policy from a new perspective. The main conclusions are as follows: firstly, compared with the ordinary VAR model, the benchmark VAR model has significant advantages in modeling efficiency, system stability and explanatory power, making it more suitable for describing the transmission mechanism of economic policy. Secondly, in terms of the role of policy uncertainty, its influence sometimes even exceeds the level impact, but in most cases, it is manifested as suppressing the output of the three industries, and the impact on industrial inflation is also dual at the same time. It shows that policy uncertainty is prone to adverse effects, and policy authorities should be more cautious in its use. Finally, from perspective of the adjustment of the three industries, tax and fee reduction are a relatively dominant means to optimize the output of the three industries, while in terms of maintaining price stability, the steady interest rate regulation is more efficient.

Molecular mechanism prediction analysis of compound kushen injection in the treatment of COVID-19 based on network pharmacology and molecular docking

Background: As one of the eight effective traditional Chinese medicines for the treatment of atypical pneumonia, compound Kushen injection (CKI) played an important role in combating pneumonia caused by severe acute respiratory syndrome coronavirus 2 virus in China in 2003. CKI is known to inhibit inflammation, and its main chemical components, namely matrine and oxymatrine, can promote Th cells to recognize and eliminate viruses. In this study, network pharmacology and molecular docking were used to explore the mechanisms of CKI for treating coronavirus disease 2019.

A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China (preprint)

The coronavirus disease 2019 (COVID-19) pandemic has killed over 0.3 million people, disrupted people’s normal lives, and severely restricted economic activities globally. In this work, a model for the next-day COVID-19 prediction in China was built based on the ensemble back-propagation neural network machine learning technique, Baidu migration index, internal travel flow index, and confirmed cases from the previous days. The 10-fold cross-validation results showed that the model performs well in estimating the next-day confirmed cases with a correlation coefficient of 0.97. To investigate the impacts of government interventions on the spread of this new coronavirus infection, the Baidu migration index and internal travel flow index multiplied by a factor of two were input into the trained machine learning model, and the results showed that the confirmed cases in the analyzed cities would increase dramatically. The correlation between the daily new confirmed cases and some meteorological factors were also analyzed, and the results revealed that these factors are not dominant in influencing the spread of this disease. Overall, the results of this work suggest that besides early diagnosis and medical treatment, a city lockdown policy is one of the most effective methods in suppressing the rapid spread of COVID-19.