Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 10 (9), e0137208

Social Vulnerability and Ebola Virus Disease in Rural Liberia


Social Vulnerability and Ebola Virus Disease in Rural Liberia

John A Stanturf et al. PLoS One.


The Ebola virus disease (EVD) epidemic that has stricken thousands of people in the three West African countries of Liberia, Sierra Leone, and Guinea highlights the lack of adaptive capacity in post-conflict countries. The scarcity of health services in particular renders these populations vulnerable to multiple interacting stressors including food insecurity, climate change, and the cascading effects of disease epidemics such as EVD. However, the spatial distribution of vulnerable rural populations and the individual stressors contributing to their vulnerability are unknown. We developed a Social Vulnerability Classification using census indicators and mapped it at the district scale for Liberia. According to the Classification, we estimate that districts having the highest social vulnerability lie in the north and west of Liberia in Lofa, Bong, Grand Cape Mount, and Bomi Counties. Three of these counties together with the capital Monrovia and surrounding Montserrado and Margibi counties experienced the highest levels of EVD infections in Liberia. Vulnerability has multiple dimensions and a classification developed from multiple variables provides a more holistic view of vulnerability than single indicators such as food insecurity or scarcity of health care facilities. Few rural Liberians are food secure and many cannot reach a medical clinic in <80 minutes. Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations. Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted. We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Distributions of district scores on seven factors.
Distribution of social vulnerability scores from factor analysis for districts classified into five clusters (using NbClust) allowing evaluation of the influence of each respective social vulnerability factor on each cluster. For each cluster of districts, vertical lines indicate the mean (central cross bar) and maximum and minimum factor scores and boxes delineate quartile factor scores across all seven factors in each cluster of districts. Factor 1- Water Quality/Medical Proximity; Factor 2- Food Quality; Factor 3- Food Quantity; Factor 4- Displaced Populations; Factor 5 –Disabled and Dependent Populations; Factor 6 –Access to Land and Free Medical Care; Factor 7- Lack of Material Goods.
Fig 2
Fig 2. Clusters of social vulnerability in rural Liberia, by district.
Based on strength and distribution of factor scores (see Fig 1), social vulnerability of each cluster of districts can be loosely ranked from most to least vulnerable as: Cluster 1, food quality, displaced persons, disabled, dependent populations; Cluster 3, food quantity, food quality, lack of access to land/free medical care; Cluster 4, food quantity, disabled dependent populations and Cluster 5, water quality/proximity to medical care; and finally, Cluster 2, no strong vulnerability scores (county boundaries are in black, district boundaries in gray, main roads in yellow).
Fig 3
Fig 3. Geographical distribution of Ebola virus disease cases in Liberia, by county.
Data are estimates made on 10 May 2015 [3]. Liberia had the second highest level of cumulative confirmed, probable, and suspected cases in West Africa as of this date, with the greatest mortality.
Fig 4
Fig 4. Percentage of households in each district that are more than 80 minutes travel to a healthcare facility.
Travel by the rural population is primarily by foot, bicycle, motorbike, bush taxi, truck, or some combination. Organized public transportation is lacking and many roads are impassable in the rainy season.

Similar articles

See all similar articles

Cited by 9 PubMed Central articles

See all "Cited by" articles


    1. Saéz AM, Weiss S, Nowak K, Lapeyre V, Zimmermann F, Düx A, et al. Investigating the zoonotic origin of the West African Ebola epidemic. EMBOMM, 2015; 7: 17–23. - PMC - PubMed
    1. World Health Organization Ebola Response Team. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Eng J Med 2014; 371: 1481–1495. - PMC - PubMed
    1. World Health Organization. Ebola response roadmap situation report 13 May 2015 [internet]. Geneva: World Health Organization; Available: Accessed 18 May 2015.
    1. Bausch DG, Schwarz L. Outbreak of Ebola virus disease in Guinea: Where ecology meets economy. PLoS Negl Trop Dis 2014; 8: e3056 Available: Accessed 10 December 2014. 10.1371/journal.pntd.0003056 - DOI - PMC - PubMed
    1. Feldmann H, Geisbert TW. Ebola haemorrhagic fever. Lancet 2011; 377: 849–862. 10.1016/S0140-6736(10)60667-8 - DOI - PMC - PubMed

Publication types

Grant support

Funding for travel was provided to JAS, SLG, and MLW by the Liberia Mission, US Agency, for International Development through the US Forest Service Office of International Programs. All other funding was internal Forest Service appropriated funding from the US Congress. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.