From viral evolution to spatial contagion: a biologically modulated Hawkes model

Mutations sometimes increase contagiousness for evolving pathogens. During an epidemic, epidemiologists use viral genetics to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen's evolution to its spatial contagion dynamics -- effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling -- and apply this \emph{phylogenetic Hawkes process} to a Bayesian analysis of 23,422 viral cases from the 2014-2016 Ebola outbreak in West Africa. With a mere 1,610 samples providing RNA data, our model is able to detect subsets of the Ebola virus with significantly elevated rates of spatiotemporal propagation.