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Lancet Infect Dis. 2015 Feb;15(2):204-11. doi: 10.1016/S1473-3099(14)71074-6. Epub 2015 Jan 7.

Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis.

Author information

1
Bruno Kessler Foundation, Trento, Italy.
2
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA.
3
Institute for Scientific Interchange, Torino, Italy.
4
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
5
Department of Biostatistics, College of Public Health, Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
6
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
7
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA; Institute for Quantitative Social Sciences at Harvard University, Cambridge, MA, USA. Electronic address: a.vespignani@neu.edu.

Abstract

BACKGROUND:

The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions.

METHODS:

We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits.

FINDINGS:

Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4-76·4) were acquired in hospitals, 30·7% (14·1-46·4) in households, and 8·6% (3·2-11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits.

INTERPRETATION:

The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates.

FUNDING:

US Defense Threat Reduction Agency, US National Institutes of Health.

PMID:
25575618
PMCID:
PMC4409131
DOI:
10.1016/S1473-3099(14)71074-6
[Indexed for MEDLINE]
Free PMC Article

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