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

TL;DR

This paper introduces a phylogenetic Hawkes process model that links viral evolution to spatial contagion, enabling detection of highly contagious viral strains during outbreaks using Bayesian analysis and high-performance computing.

Contribution

It combines phylogenetic inference with self-exciting process modeling to analyze pathogen spread and evolution in a unified framework, with scalable computational methods.

Findings

Identified viruses with elevated propagation rates during Ebola outbreak.

Developed scalable Bayesian inference methods for large genomic datasets.

Demonstrated the model's ability to detect highly contagious viral strains.

Abstract

Mutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data 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. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1,610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high performance computing framework within an adaptively preconditioned Hamiltonian Monte Carlo routine.