Management of plastic waste during sanitary emergencies requires the development of computational tools to estimate its environmental impact. The purpose of this work is to develop a simulation model to predict the plastic waste trends during sanitary emergencies, such as the COVID-19 pandemic, in urban regions of developing countries. The proposed simulation consists of two models: a municipal waste generation model and a sanitary emergency (COVID-19) dynamics prediction model. The plastic waste generation is estimated based on the population growth rate and the per capita waste generation rate, which in the model is increased for the infected population. In the second component, a Gated recurrent unit (GRU) recurrent neural network, trained with historical COVID-19 data from a specific urban region in Colombia, is used to simulate/predict the behaviour of a sanitary emergency such as an epidemic in that specific region. Recurrent neural networks have demonstrated to be a convenient tool for predicting the dynamics of a transmissible disease such as COVID-19. The GRU output is integrated into the simulation models of plastic waste generation. For the evaluation of the model, the total waste, and plastic waste generated for the last three months of 2021 in the Bucaramanga Metropolitan Area were estimated. The results show a slight increase (10 %) in plastic waste generation for the epidemic scenario that resembles the actual data. For the three months is the evaluation time interval, the ground truth average plastic waste generation per month was 9.03 t. The model forecast had a mean absolute error of 0.62 t for the three months in the evaluation time interval.