In 2019, COVID-19 emerged worldwide, and it still continues to spread. In order to prevent the spread of disease, there have been many efforts, such as developing medicine and vaccine or studying forecasting epidemic diffusion. Especially, forecasting studies help government officials take proper action in time. However, there were few forecasting studies considering the nature of the disease and the data problem in the real-world. Thus, the stochastic SIQR(susceptible-infected-quarantine-removed) model is proposed. Unlike the traditional model, the SIQR model considers asymptomatic or pre-symptomatic patients who are able to transmit the disease. This research also combines the delays that occurred in the observation to consider the data problem and in the reaction to reflect the gradual trend change. Finally, a simulation of the complex model using the Gillespie algorithm is established. This research shows that the proposed SIQR model explains COVID-19 epidemic diffusion better than the traditional SEIR(susceptible-exposed-infected-released) in terms of forecasting errors such as MAPE, RMSE and MAD.
