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

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.

Figure 1

Early spread of EVD in Liberia A Top panel: cumulative number (on a log scale) of EVD deaths over time in the general population of Liberia. Dots refer to the data reported by the WHO (dark dots indicating data used for model initialization and calibration). Lines and shaded areas refer to estimated average and 95% CI model predictions, respectively. The scenario assuming 100% reporting is shown in orange, and the 50% underreporting scenario is shown in blue. The hospitalization rate was assumed to be 80%. Middle panel: cumulative number (on a log scale) of EVD cases (confirmed, probable, and suspected) over time in the general population. Colours as in top panel. Bottom panel: cumulative number (on a log scale) of EVD deaths over time among health care workers. Colours as in top panel. B Proportions of infections occurring within households and the community, in hospitals, and during funerals as of August 16, 2014. Results assuming 50% and 100% reporting rates in the general population are shown. C Proportion of cases among HCW and proportion of cases due to contacts between household members (HM) as of August 16, 2014 by assuming 50% and 100% reporting rates in the general population. D Simulations of the spatial spread of EVD in Liberia as of June 16, 2014. Predicted cumulative number of EVD cases per cell over time in Liberia by assuming a 100% reporting rate and 80% hospitalization rate. Each cell corresponds to an area of about 25 km². E As D but as of August 16, 2014.

Figure 2

Spatio-temporal dynamics after mid August 2014 A Number of deaths (top panel) and cases (bottom panel) in the general population. Dots refer to the data reported by the WHO. Lines and shaded areas refer to estimated average and 95% CI model predictions, respectively. Orange refers to the 100% reporting scenario, blue to 50% reporting scenario. An 80% hospitalization rate was assumed. B Left panel: daily number of admission to ETUs by assuming the 100% reporting scenario. Lines and shaded areas refer to estimated average and 95% CI model predictions, respectively. Dots refer to the data reported by the WHO. An 80% hospitalization rate was assumed. Right panel: as left panel but for the number of Ebola patients in treatment in ETUs. C Cumulative number of cases in the general population in the most affected counties of Liberia (the seven counties account for about 97% of overall cases) by assuming the 100% reporting scenario. Dots refer to the data reported by the WHO. Lines and shaded areas refer to estimated average and 95% CI model predictions, respectively.

Figure 3

Impact of non-pharmaceutical interventions. A Estimated cumulative number of deaths (boxplot show 2·5%, 25%, 75% and 97·5% quantiles of the predicted distribution) as predicted by the model by assuming the 100% reporting scenario and considering different degrees of interventions. An 80% hospitalization rate is assumed. B Estimated median number of daily deaths by assuming the 100% reporting scenario, the effects of both ETUs and safe burials, and by varying the coverage of protection kits from 50% to 90%. An 80% hospitalization rate is assumed.