ABSTRACT
Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug-disease pharmacological effect between drug-target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.
ABSTRACT
The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.
ABSTRACT
INTRODUCTION: As part of infection control measures for COVID-19, individuals have been encouraged to adopt both preventive (such as handwashing) and avoidant behavioural changes (e.g. avoiding crowds). In this study, we examined whether demographics predicted the likelihood that a person would adopt these behaviours in Singapore. METHODS: A total of 1,145 participants responded to an online survey conducted between 7 March and 21 April 2020. We collected demographic information and asked participants to report which of 17 behaviour changes they had undertaken because of the COVID-19 outbreak. Regression analyses were performed to predict the number of behavioural changes (preventive, avoidant, and total) as a function of demographics. Finally, we sought to identify predictors of persons who declared that they had not undertaken any of these measures following the outbreak. RESULTS: Most participants (97%) reported at least one behavioural change on account of the pandemic, with changes increasing with the number of local COVID-19 cases (P<0.001). Additionally, women and those who were younger adopted more preventive behaviours (gender: P<0.001;age: P=0.001). Women were more likely to increase handwashing frequency, and younger individuals were more likely to wear face masks prior to legislation. Finally, women and those who were married adopted more avoidant behaviours (gender: P<0.001;marital status: P<0.001), with both groups avoiding crowded areas and staying home more than usual. Women also voluntarily reduced physical contact, whereas those who were married preferentially chose outdoor venues and relied on online shopping. CONCLUSION: Our characterisation of behavioural changes provides a baseline for public health advisories. Moving forward, health authorities can focus their efforts on encouraging segments of the population who do not readily adopt infection control measures against COVID-19.
ABSTRACT
INTRODUCTION: Amid the COVID-19 pandemic, many rumours have emerged. Given prior research linking rumour exposure to mental well-being, we conducted a nationwide survey to document the base rate of rumour exposure and factors associated with rumour vulnerability. METHODS: Between March and July 2020, 1,237 participants were surveyed on 5 widely disseminated COVID-19 rumours (drinking water frequently could be preventive, eating garlic could be preventive, the outbreak arose because of bat soup consumption, the virus was created in an American lab, and the virus was created in a Chinese lab). For each rumour, participants reported whether they had heard, shared or believed each rumour. RESULTS: Although most participants had been exposed to COVID-19 rumours, few shared or believed these. Sharing behaviours sometimes occurred in the absence of belief;however, education emerged as a protective factor for both sharing and belief. CONCLUSION: Our results suggest that campaigns targeting skills associated with higher education (e.g. epistemology) may prove more effective than counter-rumour messages.
ABSTRACT
Background: Immunisation documentation in India has historically been fragmented over a number of systems. To reduce errors and wasted vaccinations for one of the largest and most complex public vaccination systems, data storage has migrated to electronic application-based data records. However, the system remains fragmented. There has been much interest in the global health sector about the use of blockchain technology to build secure, immutable databases for complex data in fragmented environments. As part of the Indian government's innovation initiatives, our project aims to describe a blockchain immunisation system to store immunisation data, pilot its use in India, and assess strengths, limitations, and feasibility of blockchain immunisation. Methods: A connector database—Anveshan—was developed for the state of Gujarat, India, to: (1) remove data fragmentation;(2) enable aggregate analysis to allow tracking of dynamic data;(3) track data changes;and (4) enable fault tolerance so data can be accurately preserved for long periods of time. Although live piloting was not possible because of the COVID-19 pandemic, a simulated pilot was done. A report including lessons learned, benefits and challenges of blockchain application to immunisation documentation, and recommendations for future usage was delivered. Findings: Results from the simulation showed that the Anveshan database was able to decrease data fragmentation by querying across both supply and delivery databases. Aggregate analysis capability was increased through development of a dashboard user interface that allowed policymakers and immunisation programme staff to receive real-time query feedback. Data changes could be tracked and private data encrypted by incorporating disincentives for inappropriate data modification. Blockchain technology properties increased fault tolerance, which make data less corruptible. Although blockchain technology offers benefits for data storage, it does not facilitate improvements in data collection and quality, which remain a significant challenge in India. Multiple health-care stakeholders would need to work together and have access to live data to ensure nationwide scalability. Interpretation: Our findings show the challenges and benefits of a private blockchain system and identify areas requiring continued improvement for immunisation documentation in India. One important consideration in infrastructure design on a nationwide scale is the design of the blockchain system itself. This study is an important first step to show that evidence-based design innovation using blockchain can help countries such as India to address complex public health-care delivery conundrums. Funding: India Grand Challenges grant (Bill & Melinda Gates Foundation and BIRAC, Government of India).