Epidemic intelligence aims to detect, investigate and monitor potential health threats while relying on formal (e.g. official health authorities) and informal (e.g. media) information sources. Monitoring of unofficial sources, or so-called event-based surveillance (EBS), requires the development of systems designed to retrieve and process unstructured textual data published online. This manuscript focuses on the extraction and combination of epidemiological information from informal sources (i.e. online news), in the context of the international surveillance of animal infectious diseases. The first objective of this thesis is to propose and compare approaches to enhance the identification and extraction of relevant epidemiological information from the content of online news. The second objective is to study the use of epidemiological entities extracted from the news articles (i.e. diseases, hosts, locations and dates) in the context of event extraction and retrieval of related online news. This manuscript proposes new textual representation approaches by selecting, expanding, and combining relevant epidemiological features. We show that adapting and extending text mining and classification methods improve the added value of online news sources for event-based surveillance. We stress the role of domain expert knowledge regarding the relevance and the interpretability of methods proposed in this thesis. While our researches are conducted in the context of animal disease surveillance, we discuss the generic aspects of our approaches regarding unknown threats and One Health surveillance.