Search arXivSearch

arXiv · 2007.02202

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Abstract

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu. 2021-03-11. A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19. https://arxiv.org/abs/2007.02202

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup

An individual's adaptive immune receptor (AIR) repertoire records immune history due to the receptors' antigen specificity. Reading this record requires computational approaches for inferring receptor function from sequence, as the diversity of possible receptor-antigen pairs vastly outstrips experimental knowledge. Identification of AIRs with similar sequence and thus putatively similar function is a common performance bottleneck in these approaches. Here, we benchmark the runtime scaling of five algorithmic approaches to radius-based search for Levenshtein neighbors. We show that a symmetric deletion lookup approach, originally proposed for spell-checking, is particularly scalable. We introduce SymScan and XTNeighbor, optimized CPU and GPU software implementing a parallelized variant of the algorithm. For one million input sequences, these tools identify all sequence pairs that differ by one or two edits in seconds, orders of magnitude faster than existing approaches. We demonstrate how symmetric deletion lookup can be integrated as a pre-filtering step in T cell receptor metaclone discovery and B cell receptor lineage identification. Our contribution is poised to greatly accelerate existing analysis pipelines and enable processing of immunosequencing data at scale.

q-bio.QM

Implication of modelling choices on connectivity estimation: A comparative analysis

Landscape connectivity is an important field with important conservation implications. Connectivity modelling is a useful tool to inform and guide landscape planning. However, it involves assumptions and methodological decisions which ultimately impact connectivity outcomes. In order to understand the implications of modelling choices on final connectivity estimations, we compare two landscape characterisation approaches - expert knowledge and species distribution models - and three movements models - least-cost paths, circuit theory and an individual-based movement simulator. The implementation of the models and the construction of the analyses scope highlighted conceptual and methodological differences that made the comparison difficult. Landscape characterisation appears as the principal factor determining connectivity outcomes. Therefore, the confrontation between expert knowledge and species distribution models is critical to leverage points of convergence and complementarity between these two approaches. Conceptual differences between movement models are reported on connectivity map and habitat patch contribution estimations. In the want of data and protocol design specifically to validate connectivity models, approaches that integrate stochastic and behavioural processes, bring a more realistic perspective to connectivity estimation.

q-bio.QM

Triplication: an important component of the modern scientific method

A scientific-study protocol (defined) is designed to deliver results from which inductive inference is allowed. In the nineteenth century, triplication was introduced into the plant sciences and Fisher's p<0.05 rule (1925) incorporated into triple-result protocols designed to counter random/systematic errors which contribute to real-world variability. The aims of the present study were to: (1) classify replication protocols; (2) assess their prevalence in plant-science studies (published during one twenty-first-century year; for defined variable construct); (3) explore triplication rationale. Methods: a plant-sciences protocol-prevalence report was produced; experimental/associational-study proportions analyzed; and real-world-data proxies used to show confidence-interval-width patterns with increasing replicate number. Results: 25% plant-science studies analyzed showed triplication, including 11% triple-result protocols (including greater replicate numbers: 48%;17%, respectively). Theoretical considerations indicated that even if systematic errors predominate, (previously-known) square-root rules sometimes apply, contributing to triplication importance (exemplified by real-world-data proxies). Conclusions: The defined protocols, with minor modifications, should provide the means for assessment of most sciences. Triplication was extensively applied in studies analysed and there are strong methodological reasons why triplication, rather than duplication/quadruplication, is the appropriate standard: triple-result protocols: (a) effectively reduce false positives to acceptable levels; (b) give qualitatively-different information (shape) from duplication; (c) have a large efficiency advantage (concerning confidence-interval widths) over quadruplication. The application of batch replication is not, primarily, a statistical problem and cannot effectively be replaced by simulation.

q-bio.QM