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Containing the recent West African outbreak of Ebola virus (EBOV) requiredthe deployment of substantial global resources. Operationally, health workersand surveillance teams treated cases, collected genetic samples, and trackedcase contacts. Despite the substantial progress in analyzing and modeling EBOVepidemiological data, a complete characterization of the spatiotemporal spreadof Ebola cases remains a challenge. In this work, we offer a novel perspectiveon the EBOV epidemic that utilizes virus genome sequences to informpopulation-level, spatial models. Calibrated to phylogenetic linkages, thesedynamic spatial models provide unique insight into the disease mobility of EBOVin Sierra Leone. Further, we developed a model selection framework thatidentifies important epidemiological variables influencing the spatiotemporalpropagation of EBOV. Consistent with other investigations, our results showthat the spread of EBOV during the beginning and middle portions of theepidemic strongly depended on the size of and distance between populations. Ouranalysis also revealed a substantial decline in the dependence on populationsize at the end of the epidemic, coinciding with the large-scale interventioncampaign: Operation Western Area Surge. More generally, we believe thisframework, pairing molecular diagnostics with dynamic models, has the potentialto be a powerful forecasting tool along with offering operationally-relevantguidance for surveillance and sampling strategies during an epidemic.

Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes