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, 5 (8), 825-7

Quantifying the Epidemic Spread of Ebola Virus (EBOV) in Sierra Leone Using Phylodynamics

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Quantifying the Epidemic Spread of Ebola Virus (EBOV) in Sierra Leone Using Phylodynamics

Samuel Alizon et al. Virulence.

Abstract

Measuring epidemic parameters early in an outbreak is essential to inform control efforts. Using the viral genome sequence and collection date from 78 infections in the 2014 Ebola virus outbreak in Sierra Leone, we estimate key epidemiological parameters such as infectious period duration (approximately 71 hours) and date of the first case in Sierra Leone (approximately April 25th). We also estimate the effective reproduction number, Re, (approximately 1.26), which is the number of secondary infections effectively caused by an infected individual and accounts for public health control measures. This study illustrates that phylodynamics methods, applied during the initial phase of an outbreak on fewer and more easily attainable data, can yield similar estimates to count-based epidemiological studies.

Keywords: Bayesian inference; Ebola virus (EBOV); R0; epidemiology; phylogenetics.

Figures

Figure 1.
Figure 1.
(A) Epidemiological predictions for cumulative number of cases in Sierra Leone based on the SIR birth-death (BD) model for the phylogenetic tree prior. The gray region indicates the time during which sequences were collected (from day 147 – 169) and the black dots are the actual cumulative number of cases reported through Sept 10 (Day 253). Light green lines show the trajectories of stochastic model simulations, and the solid dark blue line indicates the deterministic solution (with the dashed dark blue lines indicating 95% confidence intervals and the dotted blue lines the 50% confidence intervals). (B) The same comparison as in panel A, but using the coalescent model with exponential growth (EXP) for the phylogenetic tree prior. (C) Bayesian Skyline skyline with 95% HPD inferred using the BS coalescent model for the phylogenetic tree prior.

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