Abstract
Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Despite recent progress in analysing and modelling EBOV epidemiological data, a complete characterization of the spatio-temporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages of virus genomes, these spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone without the need for human mobility data. 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 phylodynamic analysis also revealed a change in model preference towards a spatial model with power-law characteristics in the latter portion of the epidemic, correlated with the timing of major intervention campaigns. More generally, we believe this framework, pairing molecular diagnostics with a dynamic model selection procedure, has the potential to be a powerful forecasting tool along with offering operationally relevant guidance for surveillance and sampling strategies during an epidemic.
Highlights
Arresting the West African Ebola virus (EBOV) epidemic of 2013–2016 required a significant international intervention and exposed a global vulnerability to emerging epidemics
We present a novel investigation of the EBOV genome data, allowing for a more resolved characterization of the spatiotemporal dynamics during the epidemic
The partially observed transmission network (POTN) algorithm is designed to return several possible descendants associated with the same ancestor when these are supported by the data
Summary
Arresting the West African Ebola virus (EBOV) epidemic of 2013–2016 required a significant international intervention and exposed a global vulnerability to emerging epidemics. License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited Other spatial models, such as the well-known, scale-free Levy flights, depend solely on travelling distance and have been used to describe a wide-ranging set of phenomena from epidemiology [18], ecology [19] and plasma physics [20]. Other phylodynamic analyses of EBOV incorporated multiple countries and revealed the importance of social clustering to transmission risk [34,35] Despite these recent investigations, which identify potential drivers of the EBOV epidemic, a fully characterized understanding of the spread of the West African epidemic remains a challenge. Paired with advances in phylogenetics that identify linkages between cases [8], EBOV genome data offer powerful insight into spatiotemporal, transmission events These genomic linkages, in combination with geographical and demographical characteristics included in our framework, help infer the parameters of gravity and Levy flight models. We believe that this framework could be implemented to improve forecasting efforts and help design efficient intervention campaigns that adapt to real-time phylodynamics
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
