Characterizing the Divergence Between Two Different Models for Fitting and Forecasting the COVID-19 Pandemic

EasyChair Preprint 5464

Abstract

Since the novel Coronavirus (COVID-19) has been announced as a global pandemic, researchers from different disciplines have attempted to describe and forecast the spread of COVID-19. Some recent studies try to predict the future trend of the COVID-19 pandemic by deep learning, e.g., the long short-term memory (LSTM), but most works focus on the compartmental epidemic model based curve fitting and forecast. The susceptible-infected-removed (SIR) model and the susceptible-exposed-infected-removed (SEIR) model are two most commonly used compartmental models. The question is to what extent the choice of epidemic models will affect the fitting and long-term forecast performance. In this work, we compared the fitting and prediction performance by considering and ignoring the exposed state to characterize the divergence between these two different models.

Keyphrases: COVID-19 pandemic, Exposed state, forecast

@booklet{EasyChair:5464,
  author    = {Tian Gan and Long Ma},
  title     = {Characterizing the Divergence Between Two Different Models for Fitting and Forecasting the COVID-19 Pandemic},
  howpublished = {EasyChair Preprint 5464},
  year      = {EasyChair, 2021}}