Epidemiological and evolutionary analysis of the 2014 Ebola virus outbreak

TL;DR

This study analyzes the genetic diversity and growth dynamics of Ebola virus clades during the 2014 outbreak, providing a method for real-time epidemic tracking based on genealogical data.

Contribution

It introduces a simple summary statistic-based method to infer clade-specific growth rates from genealogical trees, aiding real-time epidemic monitoring.

Findings

Major Sierra Leone clades show different growth rates and reproduction numbers.

Growth heterogeneity among clades can lead to shifts in epidemic dynamics.

The method enables real-time tracking of evolving viral populations.

Abstract

The 2014 epidemic of the Ebola virus is governed by a genetically diverse viral population. In the early Sierra Leone outbreak, a recent study has identified new mutations that generate genetically distinct sequence clades. Here we find evidence that major Sierra Leone clades have systematic differences in growth rate and reproduction number. If this growth heterogeneity remains stable, it will generate major shifts in clade frequencies and influence the overall epidemic dynamics on time scales within the current outbreak. Our method is based on simple summary statistics of clade growth, which can be inferred from genealogical trees with an underlying clade-specific birth-death model of the infection dynamics. This method can be used to perform realtime tracking of an evolving epidemic and identify emerging clades of epidemiological or evolutionary significance.