SEIR-Based Modeling of COVID-19: Comparative Evaluation of Parameter and Equation Choices

Abstract

This article compares the accuracy of susceptible‑exposed‑infected‑recovered (SEIR) model and two improved models in terms of case number fitting and peak prediction during the early stage of the epidemic in the same province (from January 2020 to March 2020), examining whether there are any issues with the models. The paper uses some models and data as examples to analyze the advantages and disadvantages of different models. That's an important task because models may not fit reality satisfactorily or need more time to calculate if the models are not good enough. In some cases, researchers even need to give up the models. In the research, the author tidied up some papers that told people how to use SEIR models and their variants to analyze coronavirus disease 2019 (COVID‑19), and the author found that in most cases, people need to decrease the number of parameters and use the real parameters. There are also a few exceptions. The scholars employed some special algorithms or other mathematical tools to enhance the accuracy of the model and effectively avoid a series of problems caused by excessive parameters and variables. However, according to the author's observation, the number of such articles is relatively small and requires a very high level of mathematical knowledge. Most articles still use fewer variables and employ actual parameters.