COVID-19 Modeling: A Review

Abstract

The SARS-CoV-2 viruses and their triggered COVID-19 pandemic have fundamentally reshaped the world in almost every aspect, their evolution and influences remain. While over a million of literature have been produced on these unprecedented, overwhelming global disaster, one critical question is open: How has COVID-19 been quantified globally? This further inspires many other questions: What COVID-19 problems have been modeled? How have modeling methods in areas such as epidemiology, artificial intelligence (AI), data science, machine learning, deep learning, mathematics and social science played their roles in characterizing COVID-19? Where are the gaps and issues of these COVID-19 modeling studies? What are the lessons for quantifying future disasters? Answering these questions involves the analysis of a very broad spectrum of literature across different disciplines and domains. Distinguishing from specific efforts, this review takes the first attempt to generate a systematic, structured and contrastive landscape and taxonomy of global COVID-19 modeling. First, the surveyed problems span over a full range of COVID-19, including epidemic transmission processes, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their influence, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. Second, the reviewed modeling methods traverse all relevant disciplines, from statistic modeling to epidemic modeling, medical analysis, biomedical analysis, AI, deep and machine learning, analytics, and simulation. Critical analyses further identify significant issues and gaps, for example, simple techniques and similar problems have been overwhelmingly addressed everywhere, while intrinsic and foundational issues and deep insights have been overlooked. The review discloses significant opportunities for more deeply, effectively and uniquely quantifying COVID-19-like global disasters from their intrinsic working mechanisms, interactions and dynamics.