Spatial spread of the West Africa Ebola epidemic

Abstract

Controlling Ebola outbreaks and planning an effective response to future emerging diseases are enhanced by understanding the role of geography in transmission. Here we show how epidemic expansion may be predicted by evaluating the relative probability of alternative epidemic paths. We compared multiple candidate models to characterize the spatial network over which the 2013–2015 West Africa epidemic of Ebola virus spread and estimate the effects of geographical covariates on transmission during peak spread. The best model was a generalized gravity model where the probability of transmission between locations depended on distance, population density and international border closures between Guinea, Liberia and Sierra Leone and neighbouring countries. This model out-performed alternative models based on diffusive spread, the force of infection, mobility estimated from cell phone records and other hypothesized patterns of spread. These findings highlight the importance of integrated geography to epidemic expansion and may contribute to identifying both the most vulnerable unaffected areas and locations of maximum intervention value.

Discrete stochastic analogs of Erlang epidemic models

“…The first is the way populations are distributed into major population centres, each of which can be assumed to constitute a homogeneous subpopulation. The second is to appropriately characterize the movement of individuals among subpopulations [32], particularly individuals who are infected, but still asymptomatic (e.g. individuals who are in class E, but make the transition to class I once they have moved).…”

“…Fitting epidemiological models to real data possess significant challenges because most epidemics do not conform to the assumptions underlying the basic SEIR formulation. In particular, populations are rarely spatially homogeneous [25,30,32], and disease transmission varies with age [55] and other individual-level factors, including behaviour [16]. An additional acknowledged weaknesses of the SEIR formulation is that if a group of n individuals enter class X (X = E or I) at time t and exit at rate γ , then the number of individuals still in state X at time t + τ is given by the exponential function n(t + τ ) = n(t) e −γ X τ [56].…”

Thermal biology of mosquito‐borne disease

“…Connecting mechanistic model predictions to independent field datamodel validationis critical for comparing different models and for assessing their applicability in the field (Hooten & Hobbs 2015). Although several potential approaches exist, including simulating data from mechanistic models to compare with observed vector abundance or case incidence (e.g., Morin et al 2015;Kramer et al 2016), or testing the accuracy of models fit to a training dataset when predicting a separate testing dataset (e.g. Smith et al 2007a;Ren et al 2016), few studies have applied existing methods to validate vector-borne disease models (but see Tompkins & Ermert 2013;Wesolowski et al 2015).…”

The ability of single genes vs full genomes to resolve time and space in outbreak analysis

“…This might explain why in many cases when comparing analysis results between genome and GP datasets statistical power in migration model remains disproportionately high despite retaining only 10% of available sites and mutations and results between the entire >1600 genome data set [11] are very similar to the reduced data set analysed here. On a similar note case numbers alone have been used to recover a gravity-like model for the spread of Ebola virus in West Africa [47] previously, further arguing that the clustering of cases in time and space contains sufficient information about the movement of Ebola virus in West Africa. The overall conclusion from our study as well as others [17] is that sequencing short genomic regions instead of whole genomes is an ill-advised practice for investigating infectious disease outbreaks in any appreciable detail across relatively short timescales.…”