COVID-19 Modeling: A Review

Abstract

The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenge our life and the society and economy. Many questions emerge, a critical one is to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort in 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, unclear but important for the 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 progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, nonpharmaceutical 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 and what gaps and directions exist for better futures.

How Have Global Scientists Responded to Tackling COVID-19?

“…Third, we aim to understand how various modeling techniques (Cao and Liu 2021), including applied mathematics, AI and data science (including shallow and deep learning and analytics) (Cao 2022), epidemic modeling, and other methods such as simulation, have contributed to quantifying COVID-19 in terms of their trending effects. We thus explore the following questions:…”

“…Perhaps, for the first time in the human scientific history, almost all countries, institutions, disciplines and scientists globally have been involved more or less in reacting to this emergent, unprecedented, significant threat and crisis to humans in a very unprecedented, immediate, intensive and continuous manner. An important research agenda is to review this mostly intensive global scientific effort (Cao and Liu 2021)…”

“…Perhaps, for the first time in the human scientific history, almost all countries, institutions, disciplines and scientists globally have been involved more or less in reacting to this emergent, unprecedented, significant threat and crisis to humans in a very unprecedented, immediate, intensive and continuous manner. An important research agenda is to review this mostly intensive global scientific effort (Cao and Liu 2021) and to create a COVID-19 publishing profile of this globalized COVID-19 research. In particular, as quantifying COVID-19 is essential for understanding and managing the pandemic, it is important to understand what modeling techniques including in mathematics, epidemiology, AI, data science, machine learning, deep learning, and social science etc.…”

“…In particular, as quantifying COVID-19 is essential for understanding and managing the pandemic, it is important to understand what modeling techniques including in mathematics, epidemiology, AI, data science, machine learning, deep learning, and social science etc. have been applied and where they have played their part in modeling COVID-193 (Cao and Liu 2021; Cao 2022).…”

“…Second, to quantify how G20 and OECD countries and regions have performed in tackling COVID-19, we explore the indication to the following questions: What are the publication number and impact of G20 countries and regions? What is the disciplinary publication impact of G20 countries and regions? What is the publication-averaged mean publication impact of G20 countries and regions? What are the publication number and mean publication impact of OECD countries and regions? Third, we aim to understand how various modeling techniques (Cao and Liu 2021), including applied mathematics, AI and data science (including shallow and deep learning and analytics) (Cao 2022), epidemic modeling, and other methods such as simulation, have contributed to quantifying COVID-19 in terms of their trending effects. We thus explore the following questions: What are the main keywords concerned in modeling COVID-19 globally? What are the main modeling keywords concerned in different disciplines? What are the main keywords concerned in modeling COVID-19 in the two major economic and technical powers USA and China respectively? What are the top-50 concerned problems and their publication impact? What are the top-50 concerned problems and their modeling techniques? What are the top-50 modeling techniques and their publication impact? What are top-10 monthly concerned problems in modeling COVID-19? What are the top-10 problems concerned in the top-10 published countries? What are the top-10 modeling techniques applied by the top-10 published countries? What are the top-10 problems and techniques concerned by the top-10 published countries? Lastly, we aim to understand the relations between the COVID-19 research outcomes and a country’s coronavirus infections, deaths, and their economy by exploring the following questions: How does global first-authored countries’ GDP per capita correlate with their publications and cumulative publication impact? How does a G20 country’s GDP correlate with their publication quantity and impact? How does an OECD country’s GDP correlate with their publication quantity and impact? How does global monthly publications correlate with the global monthly infections and deaths in major disciplines? How does a G20 country’s coronavirus infections and deaths correlate with their publications? How does an OECD country’s coronavirus infections and deaths correlate with their publications? …”