A NEW STOCHASTIC SIQR (SUSCEPTIBLE-INFECTED-QUARANTINE-REMOVED) MODEL WITH TWO DELAYS: FORECASTING COVID-19 DIFFUSION

Abstract

There have been many efforts to prevent the spread of COVID-19 disease, such as developing medicine and vaccine or studying forecasting epidemic diffusion. In this study, we propose a new stochastic Susceptible-Infected-Quarantine-Removed (SIQR) model with two delays. Unlike the traditional model, the SIQR model considers asymptomatic or pre-symptomatic patients who can transmit the disease. We developed the observation delay to adjust the time differences between the true occurrence, the observation in the real world, and the reaction delay to reflect gradual changes in diffusion trends. Finally, we built a simulation of the complex model using the Gillespie algorithm. We find that in terms of MAPE, RMSE, and MAD, the proposed SIQR model explains COVID-19 epidemic diffusion better than the traditional Susceptible-Exposed-Infected-Released (SEIR). In addition, over a relatively long-term period of time, the SIQR model shows better performance compared to the SEIR model with two delays.