Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
Open accessAbstract
Background and objective During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic.Method A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic.Result This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead.Conclusion The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.
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Most accessible public datasets lack labels or were labeled using unreliable techniques such as deep packet inspection, which is ineffective with encrypted traffic, or port-based labeling, which is ineffective with dynamically allocated ports.
“Lalmuanawma et al. [69] showed that most of the accessible public datasets lack labels or were labeled using unreliable techniques, such as deep packet inspection, which is ineffective with encrypted traffic, or port-based labeling, which is ineffective with dynamically allocated ports.”
From Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning (2024) · cites this paper as [69]
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Experimental — accuracy not yet measuredSome similar apps in different countries have also been employed for COVID-19 tracing.
“Some similar apps in different countries have also been employed for COVID-19 tracing [176,177].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [177]
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- Authors
- Lalmuanawma, S., Hussain, J., & Chhakchhuak, L.
- Affiliations
- Mizoram University (IN)
- University of York (GB)
- Corresponding author
- Lalmuanawma, Samuel
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 139
- Pages
- 110059
- ISSN
- 0960-0779 (ISSN-L)
- PMC
- PMC7315944
- OpenAlex
- W3037163353
- Cited by
- 845
- References
- 42
- Citations by year
- 2026: 20 · 2025: 55 · 2024: 79 · 2023: 149 · 2022: 174 · 2021: 312 · 2020: 55 · 2016: 1
- FWCI
- 25.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chaos Solitons & Fractals (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- COVID-19 pandemic, SARS-CoV-2, machine learning, artificial intelligence, COVID-19 screening, disease prediction, epidemic forecasting, contact tracing, drug development, vaccine development
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