This thesis examines the potential interface that exists between health information, specifically electronic health record (EHR) systems, and notifiable disease surveillance in Canada. It aims to highlight the benefits and barriers experienced by the current national notifiable disease surveillance strategy, as well as to highlight the successes and roadblocks to the successful implementation and adoption of EHR technologies in Canada. Qualitative methodologies, which include the 16 semi-structured interviews conducted with four key stakeholder groups, including public health experts, physicians, health administrators and academics that are concerned with EHR adoption and public health were used to obtain data. Data from interviews was analysed using grounded theory methodology and then verified using member checking and other data validation methods. Emergent themes from obtained data indicated that there is a large potential for the improvement of the current notifiable disease through the use of EHR technologies: however, the barriers currently faced by both the notifiable disease surveillance system and the state of implementation and adoption of EHR technologies prevent this from occurring. These barriers include political, financial, human, security/privacy, and technology barriers. Differences between stakeholder groups were explored, and potential solutions and insights into existing barriers were provided. The information gained from this study provides insight into the efficiency of the current infectious disease surveillance system and the progress of and need for the implementation of EHRs nationwide. In addition, the results of this study provide stakeholders with a deeper understanding of the barriers facing the use of EHR technologies for infectious disease surveillance and provide a starting place to address these issues. The results of this study can help to inform policy regarding public health surveillance and EHR implementation and adoption.

Infectious diseases attract a lot of mediatic, cultural and political attention. But are those diseases like Ebola, or ‘disease x’ actually what kills us? Since 1946, the WHO is the most authoritative figure in the fights against infectious disease outbreaks. So how does the WHO maintain this power and authority after tremendous budget cuts, competition for authority, and a shift to non-communicable disease epidemiology? This thesis uses a mixed-methods approach of quantitative analysis of ‘Disease Outbreak News’ reports, and qualitative analysis of key WHO literature, to develop the alternative narrative answering those questions. This thesis found that the WHO activities surrounding the collection and distribution of data create a political and institutional environment in which the WHO seems to be the only logical solution to prevent them. Additionally, the narrative put forth by the WHO prioritizes the ‘alert and response’ and operational capabilities of the organization to further expand authority in outbreak response. This study concludes that the WHO, through the collection and distribution of knowledge, and efforts to increase operational capability as seen through the Global Outbreak Alert and Response Network (GOARN), seeks to maintain normative authority and power as an international organization. / M.A. / Globalization of trade and travel has only increased the fear of infectious disease transmission. There is a great demand for a global health security system that is alert and capable. Based on this ‘threat’ the WHO justifies their role as global health leader. The Global Outbreak Alert and Response Network (GOARN) is the system that currently acts as the operational arm of the WHO, monitoring and coordinating response to infectious disease outbreaks globally. Despite the critical role of GOARN, its day-to-day endeavors remain unexplored by the public health field. This thesis analyzes how the WHO uses GOARN and its surveillance capabilities to collect and transform data as a method to maintain normative authority, and projects a powerful narrative as the leader of ‘alert and response’. In a competitive environment with limited financial resources, the WHO has adapted in terms of surveillance and operational capability to maintain its leadership and authority in the global public health field.

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.

The Innovative Medicines Initiative Accelerated Development of VAccine beNefit-risk Collaboration in Europe (IMI ADVANCE) project aims to develop a framework for best practice methods on integrated rapid benefit/risk monitoring of vaccines in the European Union (EU). Burden of disease is one of the measures considered when estimating vaccine benefits. This study explores the use of notifiable infectious disease surveillance data for this purpose by estimating burden of invasive meningococcal disease in the EU/European Economic Area (EEA). We use the Burden of Communicable Diseases in Europe toolkit for computing disability-adjusted life years from incidence-based data retrieved from the European Surveillance System (TESSy) held at the European Centre for Disease Prevention and Control. Invasive meningococcal is a common cause of meningitis and septicaemia, with high case-fatality (~10%) and sequelae. We found that the median annual burden of invasive meningococcal disease in the EU/EEA, 2011-2015, was 3.87 DALYs per 100 000 total population (95% UI: 3.79-3.95). Children below one year of age and children below five years of age were at greatest risk of invasive meningococcal disease serogroup B with 89.15 DALYs per 100 000 stratum specific population (95% UI: 83.11-95.02) and 22.57 DALYs per 100 000 stratum specific population (95% UI: 21.03-24.12), respectively. We found that the distribution of burden of invasive meningococcal disease serogroup B differs widely between countries in the EU/EEA and consequently confirm that national assessment of the new infant meningococcal B vaccine is highly relevant.