  A major difficulty to estimate $R$ (the effective reproducing number) of
COVID-19 is that most cases of COVID-19 infection are mild or asymptomatic,
therefore true number of infection is difficult to determine. This paper
estimates the daily change of $R$ and the detection rate simultaneously using a
Bayesian model. The analysis using synthesized data shows that our model
correctly estimates $R$ and detects a short-term shock of the detection rate.
Then, we apply our model to data from several countries to evaluate the
effectiveness of public healthcare measures. Our analysis focuses Japan, which
employs a moderate measure to keep "social distance". The result indicates a
downward trend and now $R$ becomes below $1$. Although our analysis is
preliminary, this may suggest that a moderate policy still can prevent epidemic
of COVID-19.
