Dataset Information

Exploratory investigation of region level risk factors of Ebola Virus Disease in West Africa.

ABSTRACT:

Background

Ebola Virus Disease (EVD) is a highly infectious disease that has produced over 25,000 cases in the past 50 years. While many past outbreaks resulted in relatively few cases, the 2014 outbreak in West Africa was the most deadly occurrence of EVD to date, producing over 15,000 confirmed cases.

Objective

In this study, we investigated population level predictors of EVD risk at the regional level in Sierra Leone, Liberia, and Guinea.

Methods

Spatial and descriptive analyses were conducted to assess distribution of EVD cases. Choropleth maps showing the spatial distribution of EVD risk across the study area were generated in ArcGIS. Poisson and negative binomial models were then used to investigate population and regional predictors of EVD risk.

Results

Results indicated that the risk of EVD was significantly lower in areas with higher proportions of: (a) the population living in urban areas, (b) households with a low quality or no toilets, and (c) married men working in blue collar jobs. However, risk of EVD was significantly higher in areas with high mean years of education.

Conclusions

The identified significant predictors of high risk were associated with areas with higher levels of urbanization. This may be due to higher population densities in the more urban centers and hence higher potential of infectious contact. However, there is need to better understand the role of urbanization and individual contact structure in an Ebola outbreak. We discuss shortcomings in available data and emphasize the need to consider spatial scale in future data collection and epidemiological studies.

SUBMITTER: Levy B

PROVIDER: S-EPMC6250096 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.