The spread of Covid-19 is modelled in this Bachelor thesis. This is done using two compartmental epidemiological models: the SIR-model and SIRS-model. These models divide the population into three groups, namely Susceptible, Infected and Recovered. Artificial data is generated based on the stochastic version of the models. The models are applied to this artificial data and to real data of a Covid-19 wave in Delft. Bayesian statistics are used to estimate the parameters of these models. This is done by using a Markov Chain Monte Carlo (MCMC) algorithm on the different data sets. The models are compared using the Root Mean Squared Error and the Bayes factor. For modelling the Covid-19 wave as it occurred in Delft, the SIRS-model is found to be preferred over the SIR-model. This preference is based on the Root Mean Squared Error (RMSE) and the Bayes Factor. Based on a MCMC simulation, the posterior mean value of the basic reproduction number is estimated to be 1.067. This value indicates a spread of the disease instead of it dying out.