Predicting Infection of COVID-19 in Japan: State Space Modeling Approach

Kobayashi, Genya; Sugasawa, Shonosuke; Tamae, Hiromasa; Ozu, Takayuki

The number of confirmed cases of the coronavirus disease (COVID-19) in Japan has been increasing day by day and has had a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling tool based on the state space model combined with the well-known susceptible-exposed-infected (SIR) model. The model estimation and forecasting are conducted using the Bayesian methodology. The present study provides the parameter estimates of the unknown parameters that critically determine the epidemic process derived from the SIR model and prediction of the future transition of the infectious proportion including the size and timing of the epidemic peak with the prediction intervals that naturally accounts for the uncertainty. The prediction results under various scenarios reveals that the temporary reduction in the infection rate until the planned lifting of the state on May 6 will only delay the epidemic peak slightly. In order to minimize the spread of the epidemic, it is strongly suggested that an intervention is carried out for an extended period of time and that the government and individuals make a long term effort to reduce the infection rate even after the lifting.