13 General Introduction 1 of infectious diseases. While the primary focus of this thesis is respiratory viruses, some chapters focus on other infectious diseases. However, most of the methods used in this thesis will be applicable to a broad spectrum of infectious diseases, and are not limited to respiratory viruses. The first part of this thesis focuses on population level surveillance and answers questions regarding the incidence of viral outbreaks. The second part of this thesis focuses on clinical applications, and aims to answer questions concerning risk factors and treatment of seasonal and pandemic viruses on the patient level. The third part of this thesis, the aim is to improve the efficiency of causal inference (and therefore, the efficiency of clinical trials) in infectious disease research, by improving the assessment of the outcome of interest, and by removing noise from heterogeneous diseases. Infectious disease surveillance While the most famous example of early infectious disease surveillance is perhaps John Snow’s London Cholera study in 185437, there are even earlier examples, like John Graunt’s plague surveillance study from 166238 and James Moore’s smallpox study from 18178. Over the years, infectious disease surveillance systems have become more elaborate and sophisticated, and private companies, such as Google, have tested new methods for disease surveillance8,39. Infectious disease surveillance, defined as the monitoring of the health of a population, often using epidemiological tools, is a critical aspect of public health5. There are three goals of infectious disease surveillance:5 1. Describing the current status and burden of disease 2. Monitoring of trends 3. Identifying outbreaks and novel pathogens: surveillance is essential to recognise and mitigate new pandemics14 Traditional infectious disease surveillance systems have several downsides. It is labour intensive and therefore expensive, and there is typically a time lag due to this intensive data collection process7. These time lags in data collection can lead to a higher number of infections, as a rapid response is needed with outbreaks5. The emergence of large electronic datasets has made disease surveillance significantly easier40. Examples are using medical claims data or collect data from EHR for disease tracking8 and outbreak detection41. Infectious disease surveillance systems ideally have (near) real-time collection and analysis of data, have granular geographical data and are representative of the population7,8. Well-functioning surveillance systems can be used for outbreak detection, monitoring or prediction, changes in the characteristics of patients
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