# Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes

## TL;DR

This study combines virus genome sequencing with spatial modeling to analyze Ebola's spread in Sierra Leone, providing insights into disease mobility and informing surveillance strategies during the epidemic.

## Contribution

It introduces a novel framework that integrates phylogenetic data with dynamic spatial models to understand Ebola's spatiotemporal spread and key epidemiological factors.

## Key findings

- Ebola spread depended on population size and distance early in the epidemic.
- Large-scale interventions reduced dependence on population size.
- The framework can improve epidemic forecasting and surveillance strategies.

## Abstract

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Operationally, health workers and surveillance teams treated cases, collected genetic samples, and tracked case contacts. Despite the substantial progress in analyzing and modeling EBOV epidemiological data, a complete characterization of the spatiotemporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic that utilizes virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages, these dynamic spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone. Further, we developed a model selection framework that identifies important epidemiological variables influencing the spatiotemporal propagation of EBOV. Consistent with other investigations, our results show that the spread of EBOV during the beginning and middle portions of the epidemic strongly depended on the size of and distance between populations. Our analysis also revealed a substantial decline in the dependence on population size at the end of the epidemic, coinciding with the large-scale intervention campaign: Operation Western Area Surge. More generally, we believe this framework, pairing molecular diagnostics with dynamic models, has the potential to be a powerful forecasting tool along with offering operationally-relevant guidance for surveillance and sampling strategies during an epidemic.