  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.
