Regional spread of Ebola virus, West Africa, 2014.
Ontology highlight
ABSTRACT:
SUBMITTER: Rainisch G
PROVIDER: S-EPMC4344291 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
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Project description:The 2014-2016 Ebola virus (EBOV) disease outbreak affected over 29000 people and left behind the biggest cohort (over 17000 individuals) of Ebola survivors in history. Although the persistence of EBOV in body fluids of survivors was reported before the recent outbreak, new evidence revealed that the virus can be detected up to 18 months in the semen, which represents the biggest risk of Ebola resurgence in affected communities. In this study, we review the knowledge on the Ebola flare-ups that occurred after the peak of the 2014-2016 Ebola epidemic in West Africa.
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Project description:In 2014-2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.
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Project description:Here, we report the complete genome sequences, including the genome termini, of three Ebola virus isolates (species Zaire ebolavirus) originating from Guinea that are now being widely used in laboratories in North America for research regarding West African Ebola viruses.
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Project description:Following its immergence in December 2013, the recent Zaire Ebola virus (EBOV) outbreak in West Africa has spread and persisted for more than two years, making it the largest EBOV epidemic in both scale and geographical region to date. In this study, a total of 726 glycoprotein (GP) gene sequences of the EBOV full-length genome obtained from West Africa from the 2014 outbreak, combined with 30 from earlier outbreaks between 1976 and 2008 were used to investigate the genetic divergence, evolutionary history, population dynamics, and selection pressure of EBOV among distinct epidemic waves. Results from our dataset showed that no non-synonymous substitutions occurred on the GP gene coding sequences of EBOV that were likely to have affected protein structure or function in any way. Furthermore, the significantly different dN/dS ratios observed between the 2014 West African outbreak and earlier outbreaks were more likely due to the confounding presence of segregating polymorphisms. Our results highlight no robust evidence that the 2014 EBOV outbreak is fast-evolving and adapting to humans. Therefore, the unprecedented nature of the 2014 EBOV outbreak might be more likely related to non-virological elements, such as environmental and sociological factors.
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Project description:The 2013-present Western African Ebola virus disease (EVD) outbreak is the largest ever recorded with >28,000 reported cases. Ebola virus (EBOV) genome sequencing has played an important role throughout this outbreak; however, relatively few sequences have been determined from patients in Liberia, the second worst-affected country. Here, we report 140 EBOV genome sequences from the second wave of the Liberian outbreak and analyze them in combination with 782 previously published sequences from throughout the Western African outbreak. While multiple early introductions of EBOV to Liberia are evident, the majority of Liberian EVD cases are consistent with a single introduction, followed by spread and diversification within the country. Movement of the virus within Liberia was widespread, and reintroductions from Liberia served as an important source for the continuation of the already ongoing EVD outbreak in Guinea. Overall, little evidence was found for incremental adaptation of EBOV to the human host.
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Project description:The variety of factors that contributed to the initial undetected spread of Ebola virus disease in West Africa during 2013-2016 and the difficulty controlling the outbreak once the etiology was identified highlight priorities for disease prevention, detection, and response. These factors include occurrence in a region recovering from civil instability and lacking experience with Ebola response; inadequate surveillance, recognition of suspected cases, and Ebola diagnosis; mobile populations and extensive urban transmission; and the community's insufficient general understanding about the disease. The magnitude of the outbreak was not attributable to a substantial change of the virus. Continued efforts during the outbreak and in preparation for future outbreak response should involve identifying the reservoir, improving in-country detection and response capacity, conducting survivor studies and supporting survivors, engaging in culturally appropriate public education and risk communication, building productive interagency relationships, and continuing support for basic research.
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Project description:The quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries.
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Project description: Not available
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Project description:BACKGROUND:The Ebola outbreak in West Africa has infected at least 27,443 individuals and killed 11,207, based on data until 24 June, 2015, released by the World Health Organization (WHO). This outbreak has been characterised by extensive geographic spread across the affected countries Guinea, Liberia and Sierra Leone, and by localized hotspots within these countries. The rapid recognition and quantitative assessment of localised areas of higher transmission can inform the optimal deployment of public health resources. METHODS:A variety of mathematical models have been used to estimate the evolution of this epidemic, and some have pointed out the importance of the spatial heterogeneity apparent from incidence maps. However, little is known about the district-level transmission. Given that many response decisions are taken at sub-national level, the current study aimed to investigate the spatial heterogeneity by using a different modelling framework, built on publicly available data at district level. Furthermore, we assessed whether this model could quantify the effect of intervention measures and provide predictions at a local level to guide public health action. We used a two-stage modelling approach: a) a flexible spatiotemporal growth model across all affected districts and b) a deterministic SEIR compartmental model per district whenever deemed appropriate. FINDINGS:Our estimates show substantial differences in the evolution of the outbreak in the various regions of Guinea, Liberia and Sierra Leone, illustrating the importance of monitoring the outbreak at district level. We also provide an estimate of the time-dependent district-specific effective reproduction number, as a quantitative measure to compare transmission between different districts and give input for informed decisions on control measures and resource allocation. Prediction and assessing the impact of control measures proved to be difficult without more accurate data. In conclusion, this study provides us a useful tool at district level for public health, and illustrates the importance of collecting and sharing data.
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