Big Data Technology in Infectious Diseases Modeling, Simulation and Prediction After the COVID-19 Outbreak: A Survey

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improve the structure and data of the compartment model or use agents and AI-based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and AI-related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, NPIs, ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods, to quantitatively analyze the impact of social systems, and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review starts with a research structure of research purpose, factor, data, model, and conclusion. focusing on the post-COVID-19 infectious disease prediction simulation research, summarizes various improvement methods, and analyzes matching improvements for various specific research purposes.