Digital Epidemiology

Abstract

Computational social science has had a profound impact on the study of health and disease, mainly by providing new data sources for all of the primary Ws-what, who, when, and where-in order to understand the final “why” of disease. Anonymized digital trace data bring a new level of detail to contact networks, search engine and social media logs allow for the now-casting of symptoms and behaviours, and media sharing informs the formation of attitudes pivotal in health decision-making. Advances in computational methods in network analysis, agent-based modelling, as well as natural language processing, data mining, and time series analysis allow both the extraction of fine-grained insights and the construction of abstractions over the new data sources. Meanwhile, numerous challenges around bias, privacy, and ethics are being negotiated between data providers, academia, the public, and policymakers in order to ensure the legitimacy of the resulting insights and their responsible incorporation into the public health decision-making. This chapter outlines the latest research on the application of computational social science to epidemiology and the data sources and computational methods involved and spotlights ongoing efforts to address the challenges in its integration into policymaking.