This paper tackles the epidemiological event extraction task applied to digitized documents. Event extraction is an information extraction task that focuses on identifying event mentions from textual data. In the context of event-based health surveillance from digitized documents, several key issues remain challenging in spite of great efforts. First, image documents are indexed through their digitized version and thus, they may contain numerous errors, e.g. misspellings. Second, it is important to address international news, which would imply the inclusion of multilingual data. To clarify these important aspects of how to extract epidemic-related events, it remains necessary to maximize the use of digitized data. In this paper, we investigate the impact of working with digitized multilingual documents with dierent levels of synthetic noise over the performance of an event extraction system. This type of analysis, to our knowledge, has not been alleviated in previous research.