EPIDEMIOLOGY IN REVIEW: LEVERAGING COMPUTATIONAL TOOLS AND TECHNIQUES IN EPIDEMIOLOGICAL INVESTIGATIONS FOR ACTIONABLE INSIGHTS.

ENGLISH

Epidemiology is the study of disease in a population and the factors that determine its occurrence. In addition, it involves the application of this knowledge in the control of diseases and other health problems. Over the decades, there have been more than 335 emerging infectious disease events with 75% of these events being zoonotic while 60-61% of all infectious diseases affect humans. These developments warrant the need to adequately comprehend the epidemiological triad of disease causation (host, agent, and environment) and how their various interactions influence the emergence and spread of infectious or zoonotic diseases. This interaction not only implies a changing temporality but also aligns with the One-Health initiative, fostering collaboration among various disciplines and promoting a holistic approach to surveillance, control, and outbreak preparedness. However, there remains the constant challenge of preventing the re-emergence of known pathogens and the emergence of new ones, while developing comprehensive strategies to mitigate future outbreaks. Epidemiological concepts and theories have evolved over the centuries, but statistical/quantitative epidemiology has shown promising potential in tackling some of these challenges. In the 17th century, the application of this method resulted in the eradication of diseases such as bovine pleuropneumonia and rinderpest in Britain and the United States. However, conventional statistical methods are limited in their ability to identify subtle interactions and relationships underlying disease outbreaks, efficiently analyze exponentially and ever-growing healthcare data, and rapidly process data for timely identification and response as well as implementation of data-driven strategies. Therefore, this thesis presents possible solutions to tackle these challenges such as the application of systematic reviews and meta-analyses, geospatial analyses, and molecular epidemiology in immune prophylaxis, and proposes a machine- and deep-learning architecture for identifying subtle interactions of disease determinants and making prediction on future outbreaks while applying this knowledge in disease prevention and control.