Quantifying human behavior in epidemiology

Emergent epidemics present major challenges to both global health and international politics. The 2014-2015 Ebola outbreak in West Africa alone took over ten thousand lives despite international aid nearing $5 billion from 70 countries. In retrospect, the declaration of the Public Health Emergency of International Concern came late, over 4 months after the first international transmission event. These apparent systemic failures likely reflect the fact that emergent epidemics are incredibly difficult to predict. The last decade saw Ebola emerge in the Democratic Republic of Congo in 2018, 2017, 2014 and 2012, as well as Uganda in 2007, but these previous outbreaks never exceeded a few hundred cases, compared to nearly 30,000 cases in the West African epidemic.

A key problem is that the dynamics of emergent epidemics are shaped in large part by societal and behavioral factors, which are all highly variable. We will discuss these behavioral factors using stories from local communities, contact tracing, genomic data, and reports from the most extensive social mobilization effort to date. All of these distinct data sources influence how we use and interpret models from epidemiology, network theory and collective behavior. Slowly but surely, this synergy data sources and modeling approaches should pave the way for a new approach to epidemiology.