Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis
Authored by Unknown
Date Published: 2015
DOI: 10.1016/s1473-3099(14)71074-6
Sponsors:
United States National Institutes of Health (NIH)
US Defense Threat Reduction Agency
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
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.
Tags
Dynamics
transmission
Pandemic influenza
Strategies
West-africa
Hemorrhagic-fever
Congo