mlsanityData to 7 Oct 2026

COVID-19 Modeling: A Review

arXiv 2104.12556 ↗First posted 16 Apr 2021cs.CY cs.LGcited by 7

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Abstract (arXiv)

The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper provides a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 problems, data and objectives. It constructs a research landscape of COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods, and hybrid modeling.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 51 of its 313 references that are in the map. First posted on arXiv 16 Apr 2021.

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51 of 313 references.

References · 51

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

7 papers.

  1. 2023Unbalanced Diffusion Schr"odinger Bridgecited by 9
  2. 2021A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USAcited by 7
  3. 2022Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecastingcited by 7
  4. 2023Approaching epidemiological dynamics of COVID-19 with physics-informed neural networkscited by 7
  5. 2023Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Treescited by 2
  6. 2021Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcementcited by 0
  7. 2026DeepL'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Seriescited by 0