Viral infections research in a data driven era: infectious disease surveillance and real-world causal inference

Abstract

Outbreaks of viral infections have shaped human history. From the influenza outbreak of 1918-1919 to the recent COVID-19 pandemic, respiratory viruses have had a profound impact on our health and society. The increasing availability of large-scale datasets containing detailed information on large populations has expanded our understanding of (viral) infectious diseases. New pandemics are inevitable, and a key challenge during outbreaks is coordinating research efforts and optimizing the use of available resources. In specific cases, observational datasets can be used to answer causal questions. Especially during (viral) infectious disease outbreaks—when there is an urgent need for rapid results to inform clinical practice and policy—it may be preferable to use routinely collected observational data. This dissertation demonstrates how various (real-world) data sources can enhance the speed and feasibility of infectious disease surveillance, answer clinical causal questions using observational data, and facilitate causal inference. This is achieved by reducing noise caused by heterogeneous disease presentations and by establishing outcome measures with greater precision.