Large scale natural disasters involve budgetary problems for governments. Prioritiz-1ing investment requires near real time information about the impact of the hazard in2different locations. However, such information is not available through sensors or3other devices specially in developing countries that do not have such infrastructure.4In this work, we use mobile phone activity data to infer the affected zones in the5Ecuadorian province of Manabí, after the 2016 earthquake, with epicenter in the6same province. We calculate a series of features to train a classifier based on the7K-Nearest Neighbors algorithm to detect affected zones with a 75% of precision.8We compare our results with official reports published two months after the disaster