Traditional disease surveillance can be augmented with a wide variety of open sources, such as online news media, twitter, blogs, and web search records. Rapidly increasing volumes of these open sources are proving to be extremely valuable resources in helping analyze, detect, and forecast outbreaks of infectious diseases, especially new diseases or diseases spreading to new regions. However, these sources are in general unstructured (noisy) and construction of surveillance tools ranging from real-time disease outbreak monitoring to construction of epidemiological line lists involves considerable human supervision. Intelligent modeling of such sources using text mining methods such as, topic models, deep learning and dependency parsing can lead to automated generation of the mentioned surveillance tools. Moreover, real-time global availability of these open sources from web-based bio-surveillance systems, such as HealthMap and WHO Disease Outbreak News (DONs) can aid in development of generic tools which will be applicable to a wide range of diseases (rare, endemic and emerging) across different regions of the world. In this dissertation, we explore various methods of using internet news reports to develop generic surveillance tools which can supplement traditional surveillance systems and aid in early detection of outbreaks. We primarily investigate three major problems related to infectious disease surveillance as follows. (i) Can trends in online news reporting monitor and possibly estimate infectious disease outbreaks? We introduce approaches that use temporal topic models over HealthMap corpus for detecting rare and endemic disease topics as well as capturing temporal trends (seasonality, abrupt peaks) for each disease topic. The discovery of temporal topic trends is followed by time-series regression techniques to estimate future disease incidence. (ii) In the second problem, we seek to automate the creation of epidemiological line lists for emerging diseases from WHO DONs in a near real-time setting. For this purpose, we formulate Guided Epidemiological Line List (GELL), an approach that combines neural word embeddings with information extracted from dependency parse-trees at the sentence level to extract line list features. (iii) Finally, for the third problem, we aim to characterize diseases automatically from HealthMap corpus using a disease-specific word embedding model which were subsequently evaluated against human curated ones for accuracies. / Ph. D. / Infectious Disease Outbreaks are a threat to public health and economic stability of many countries. Traditional Disease Surveillance data released by organizations, such as CDC, ProMED is delayed and therefore, not reliable for real-time monitoring of infectious disease outbreaks. Recently, open source indicators, such as online news sources and social media sources (Twitter) have been shown to be effective in monitoring infectious disease outbreaks in real-time due to their volume, ease of availability and citizen participation. This dissertation focuses on developing multiple data analytic tools which perform automated analysis of online disease-related news articles with an aim to characterize infectious diseases and monitor their spatial and temporal progression in real-time. We show that temporal trends extracted from online news articles can be used to capture dynamics of multiple disease outbreaks, such as whooping cough outbreak in U.S. during summer of 2012, periodic outbreaks of H7N9 in China during 2013-2014 and emerging MERS outbreak in Saudi Arabia. However, online news reporting during infectious disease outbreaks is driven by interest and therefore, news coverage for certain diseases can be inconsistent over time leading to erroneous surveillance.