ABSTRACT
For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.
ABSTRACT
A nationwide survey of 162,738 firms in Vietnam asked firms to report the impact of the COVID-19 pandemic on the health of the business, coping strategies used, and various firm and situational characteristics. More than 80% of firms reported negative impacts from the pandemic with fewer than 4% reporting positive effects;63% of the firms adopted at least one coping strategy. The coping strategies were categorized into seven groups as follows: (1) Non-adoption, (2) promoting e-commerce, (3) transforming key products/services, (4) training employees to improve professional qualifications, (5) finding new markets for input materials, (6) finding markets for products outside of the traditional market, (7) producing new products/services according to market demand during the epidemic period, and (8) other strategies. A multinomial logit regression model showed statistically significant associations between a firm's selected coping strategy and several independent variables, as follows: (1) Firm size, (2) impact of the pandemic on firm health, firm access to inputs, and firm access to domestic markets, (3) decrease in firm revenue, and (4) receipt of government support. However, many businesses have not implemented coping strategies, leading to concerns regarding their resilience to upcoming threats and uncertainties.
ABSTRACT
In response to a call for help during a surge in coronavirus disease-19 (COVID-19) cases in Ho Chi Minh City in July 2021, the University of Medicine and Pharmacy at Ho Chi Minh City developed and implemented a community care model for the management of patients with COVID-19. This was based on three main principles: home care; providing monitoring and care at a distance; and providing timely emergency care if needed. One team supported patients at home with frequent contacts and remote monitoring, while a second team transferred and cared for patients requiring treatment at field emergency care facilities. COVID-19-related mortality rates at the two districts where this approach was implemented (0.43% and 0.57%) were substantially lower than the overall rate in Ho Chi Minh City over the same period (4.95%). Thus, utilization of a community care model can increase the number of patients with COVID-19 who can be effectively managed from home, and use of field emergency care facilities limited the number of patients that had to be referred for tertiary care. Importantly, the community care model also markedly reduced the mortality rate compared with traditional methods of COVID-19 patient management.