The article “The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks” written by Rafael Ballester-Ripoll and Manuele Leonelli has been published in the International Journal of Approximate Reasoning. The article proposes an algorithm combining automatic differentiation and exact inference to calculate the sensitivity measures in a single pass efficiently, used in two case studies: the first modeling the country risks of a humanitarian crisis, the second studying the relationship between the use of technology and the psychological effects of forced social isolation during the COVID-19 pandemic. The article can be found at https://www.sciencedirect.com/science/article/abs/pii/S0888613X23000609. The software is implemented using the popular machine learning PyTorch library and is available at https://github.com/rballester/yodo.