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IDM publishes paper on Identifying spatio-temporal dynamics of Ebola in Sierra Leone using virus genomes in Journal of the Royal Society Interface
IDM is excited to announce the publication of the work by Kyle Gustafson and Joshua Proctor on identifying spatio-temporal dynamics of Ebola in Sierra Leone using virus genomes.
The West African Ebola virus outbreak of 2013-2016 highlighted the deadly threat posed by emerging epidemic and pandemic diseases in our highly-connected world. Researchers at the Institute for Disease Modeling have used publicly-available genome sequences to investigate the behavior of the Ebola epidemic as it swept across Sierra Leone. Probabilistic spatial models have revealed that, during the later stages of the epidemic, larger population centers became less influential to the spatial spread of the Ebola virus. This new framework is fast and adaptable, facilitating the rapid modeling of genome sequencing data.
Spatiotemporal models for virus outbreaks using genomes
Data-driven modeling of the spatiotemporal dynamics of the 2013-2016 Ebola epidemic has yielded new insights into the outbreak. The left panel illustrates a set of genetically-linked Ebola cases appearing in different chiefdoms across Sierra Leone. The heat map (right panel) indicates the probability of a new Ebola case being genetically linked to a case in Freetown, the capital and largest city. Spatial models of the epidemic based on these genetic linkages identify a changing behavior of the Ebola outbreak: population centers influence the early and middle portion of the epidemic. The population effect decreases significantly in the final time period. This adaptive, model-based framework, enabled by virus genome data, could help guide interventions during the next outbreak.
Credit: Kyle B. Gustafson and Joshua L. Proctor
Institute for Disease Modeling email@example.com
To read the full article, visit the Journal of The Royal Society Interface.