  The data for the Ebola outbreak that occurred in 2014-2016 in three countries
of West Africa are analysed within a common framework. The analysis is made
using the results of an agent based Susceptible-Infected-Removed (SIR) model on
a Euclidean network, where nodes at a distance $l$ are connected with
probability $P(l) \propto l^{-\delta }$, $\delta$ determining the range of the
interaction, in addition to nearest neighbors. The cumulative (total) density
of infected population here has the form $R(t) =
\frac{a\exp(t/T)}{1+c\exp(t/T)}$, where the parameters depend on $\delta$ and
the infection probability $q$. This form is seen to fit well with the data.
Using the best fitting parameters, the time at which the peak is reached is
estimated and is shown to be consistent with the data. We also show that in the
Euclidean model, one can choose $\delta$ and $q$ values which reproduce the
data for the three countries qualitatively. These choices are correlated with
population density, control schemes and other factors. Comparing the real data
and the results from the model one can also estimate the size of the actual
population susceptible to the disease. Rescaling the real data a reasonably
good quantitative agreement with the simulation results is obtained.
