COVID-19 Modeling: A Review

TL;DR

This paper reviews the diverse modeling approaches used to understand and combat COVID-19, covering transmission, diagnosis, interventions, and societal impacts, highlighting progress, gaps, and future opportunities.

Contribution

It provides a comprehensive categorization and comparison of COVID-19 modeling methods across multiple domains, identifying key challenges and research opportunities.

Findings

Diverse modeling techniques have been applied to COVID-19, including mathematical, statistical, AI, and social science methods.

Progress has been made in modeling transmission, diagnosis, and intervention effects, but gaps remain in integrated approaches.

The review highlights the importance of hybrid models and interdisciplinary collaboration for future research.

Abstract

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.