Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query driven, top-down execution model for predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be modeled in s(CASP) and how its expressiveness makes it pos- sible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving dense time and fluents.