Big data technology in infectious diseases modeling,simulation,and prediction after the COVID-19 outbreak

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 im-proved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems.In terms of modeling methods,the researchers use compartment subdivi-sion,dynamic parameters,agent-based model methods,and artificial intelligence related methods.In terms of factors studied,the researchers studied 6 categories:human mobility,nonpharmaceutical interventions(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 started with a research structure of research purpose,factor,data,model,and conclusion.Focusing on the post-COVID-19 infectious disease prediction simulation research,this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

Discrete time forecasting of epidemics

Forecasting in the domain of infectious diseases aims at estimating the number of cases ahead of time during an epidemic,hence fundamentally requires understanding its dynamics.In fact,estimates about the dynamics help to predict the number of cases in an epidemic,which will depend on determining a few of defining factors such as its starting point,the turning point,growth factor,and the size of the epidemic in total number of cases.In this work a phenomenological model deals with a practical aspect often disregarded in such studies,namely that health surveillance produces counts in batches when aggregated over discrete time,such as days,weeks,months,or other time units.This model enables derivation of equations that permit both estimating key dynamics parameters and forecasting.Results using both severe acute respiratory illness data and synthetic data show that the forecasting follows very well over time the dynamics and is resilient with statistical noise,but has a delay effect due to the discrete time.