The SARS-CoV-2 pandemic had taken the world by surprise since its discovery on December 2019, causing major losses worldwide. In this work, a deep learning model was developed to predict and forecast the daily SARS-CoV-2 cases on the Peruvian regions. The data used belongs to the open covid–19 data set, sourced by the Health Ministry of Peru (MINSA). The data set includes the periods from March 03, 2020 to March 16, 2021. A holdout approach was used, creating a training and validation data splits. Using the validation set, a temporal convolution neural network (TCN) composed by five layers was developed. The model was design to predict a mean tendency alongside with a prediction interval. To find the best hyper parameter configuration, a Bayesian approach was applied over the validation set. The TCN model was trained using the optimal configurati...