Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity,
D
, or the cell proliferation rate,
λ
. Estimating
D
and
λ
is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating
D
and
λ
have been proposed, these previous methods lead to point estimates of
D
and
λ
, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify
D
and
λ
using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of
D
and
λ
from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of
D
and
λ
. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating
D
and
λ
, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.
Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation
