  We study the datafor the cumulative as well as daily number of cases in the
Covid-19 outbreak in China. The cumulative data can be fit to an empirical form
obtained from a Susceptible-Infected-Removed (SIR) model studied on an
Euclidean network previously. Plotting the number of cases against the distance
from the epicenter for both China and Italy, we find an approximate power law
variation with an exponent $\sim 1.85$ showing strongly that the spatial
dependence plays a key role, a factor included in the model. We report here
that the SIR model on the Eucledean network can reproduce with a high accuracy
the data for China for given parameter values, and can also predict when the
epidemic, at least locally, can be expected to be over.
