In Mexico quick-counts take place whenever major elections take place. For the quick-counts a probabilistic sample of polling stations is selected in advance and estimates are presented in the election night. The complete samples are rarely available to publish the results in a timely manner hence the results are announced using partial samples which have biases. We developed a Bayesian hierarchical model that includes demographic and geographic covariates, the model reduces the biases associated to such covariates. The model was used, among others, for the [2018 quick-count](https://portal.ine.mx/voto-y-elecciones/conteos-rapidos-ine/) organized by the electoral authority. [Here](https://jovial-jepsen-cf1904.netlify.com) we explain the model in detail and [here](https://github.com/tereom/quickcountmx) is an R package with the model implementation, and [here](https://link.springer.com/chapter/10.1007/978-3-030-31551-1_1) is a published paper with the methodology for one of the models we used.