A LITERATURE REVIEW OF PREDICTION MODELS FOR THE TRANSMISSION OF COVID-19

Qin Zhang ； Wen Dong ； Weina Wang

《IETI Transactions on Social Sciences and Humanities》 22卷 (2023/03) Pp. 59-65

https://doi.org/10.6896/IETITSSH.202303_22.0006

摘要

Since the end of 2019, novel coronavirus pneumonia (COVID-19) was first discovered in Wuhan, China, and rapidly spread globally, causing a pandemic. Predicting the transmission trend of COVID-19 has become a research hotspot in order to control the virus spread and provide reference for government prevention and control measures. Based on literature review of COVID-19 transmission trend prediction methods in the past three years, this paper divides prediction methods into three categories: transmission dynamic models, time series models, and machine learning models. Firstly, the characteristics of the novel coronavirus and the impact of the epidemic are presented. Secondly, starting from the principles and characteristics of the three prediction models, this paper introduces in detail the modifications made by various researchers to the classical models and analyzes the advantages and disadvantages of their research achievements. Finally, this paper summarizes the three types of models from the perspectives of their characteristics and limitations.

關鍵字

COVID-19 ； Data science ； Forecasting ； Healthcare management ； Models