ABSTRACT
Using data from 112 countries from 1998 to 2018, this study quantifies the overall impact of epidemics on innovation and identifies the underlying channels. Our results show that a 1% increase in the severity of an epidemic can significantly lead to a 0.059% decrease in patent applications and a 0.092% decrease in trademark applications. This negative impact can be explained by GDP, population and R & D expenditure channels. Further, our findings indicate that an increase in international personnel exchange (IPE) and foreign direct investment (FDI) can mitigate this negative impact. Finally, we discuss the policy implications of our results.
ABSTRACT
Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness.