COVID-19 Modeling: A Review

Abstract The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenged our way of life, society and the economy. Many questions emerge, a critical one being how to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort that has been in relation to modeling COVID-19, what COVID-19 issues have been modeled? What and how well have epidemiology, AI, data science, machine learning, deep learning, mathematics and social science characterized the COVID-19 epidemic? what are the gaps and opportunities of quantifying the pandemic? Such questions involve a wide body of knowledge and literature, which are unclear but important for present and future health crisis quantification. Here, we provide a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 processes, data, mitigation and impact. With a research landscape of COVID-19 modeling, we further categorize, summarize, compare and discuss the related methods and the progress which has been made in 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 review shows how modeling methods such as 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 have addressed the COVID-19 challenges, what gaps exist and what research directions can be followed for a better future.