Ebola Cases and Health System Demand in Liberia

Drake, John M.; Kaul, RajReni B.; Alexander, Laura W.; O’Regan, Suzanne M.; Kramer, Andrew M.; Pulliam, J. Tomlin; Ferrari, Matthew J.; Park, Andrew

doi:10.1371/journal.pbio.1002056

Cited by 71 publications

(66 citation statements)

References 24 publications

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“…While all models assumed fit parameters to be constant for the time period considered, the best model nevertheless generated small-scale predictions for realized disease spread after 1 October that align well with data on administrative units infected after this time, showing the usefulness of this model even after the epidemic had peaked [38]. Because this model does not explicitly include information on the number of cases in administrative units [18] or other factors like treatment facilities [39], any discernible effects of these factors are instead included in the estimated effects of distance and population density.…”

Section: Discussionmentioning

confidence: 80%

Spatial spread of the West Africa Ebola epidemic

et al. 2016

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Controlling Ebola outbreaks and planning an effective response to future emerging diseases are enhanced by understanding the role of geography in transmission. Here we show how epidemic expansion may be predicted by evaluating the relative probability of alternative epidemic paths. We compared multiple candidate models to characterize the spatial network over which the 2013–2015 West Africa epidemic of Ebola virus spread and estimate the effects of geographical covariates on transmission during peak spread. The best model was a generalized gravity model where the probability of transmission between locations depended on distance, population density and international border closures between Guinea, Liberia and Sierra Leone and neighbouring countries. This model out-performed alternative models based on diffusive spread, the force of infection, mobility estimated from cell phone records and other hypothesized patterns of spread. These findings highlight the importance of integrated geography to epidemic expansion and may contribute to identifying both the most vulnerable unaffected areas and locations of maximum intervention value.

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Section: Discussionmentioning

confidence: 80%

Spatial spread of the West Africa Ebola epidemic

et al. 2016

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“…This association might have been partially due to surveillance bias: i.e., cases near hospitals were more likely to be diagnosed and reported. On the other hand, nosocomial transmission potentially played a role in the geographical expansion of EVD (14,23,24). The proportion of healthcare workers among all confirmed cases was relatively high in June−August 2014 (SI Appendix, Fig.…”

Section: Discussionmentioning

confidence: 99%

“…S2). Patients who had EVD-like symptoms caused by other pathogens and visited hospitals with EBOV-infected cases were also at high risk of infection (24). In addition, chiefdoms with sustained outbreaks (patterns IV, V, and VI) were mainly found in densely populated regions where main roads and hospitals cluster (Figs.…”

Section: Discussionmentioning

confidence: 99%

Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone

Fang

Yang

Jiang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

101

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Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65% (95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014−2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission.Ebola virus disease | spatiotemporal modeling | household transmission | secondary attack rate | intervention effectiveness

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“…One approach to control such infections is through the implementation of cross-reactive vaccines, which are currently under development (85). When vaccines are not yet available, as was the case for Ebola virus during most of the 2014–2015 epidemic, reducing human-human transmission through contact tracing and subsequent quarantine and treatment can help to limit epidemic spread (86). …”

Section: How Community Ecology Can Help Manage Infectious Diseasesmentioning

confidence: 99%

Why infectious disease research needs community ecology

Johnson

Roode

Fenton

2015

Science

363

361

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Infectious diseases often emerge from interactions among multiple species and across nested levels of biological organization. Threats as diverse as Ebola virus, human malaria, and bat white-nose syndrome illustrate the need for a mechanistic understanding of the ecological interactions underlying emerging infections. We describe how recent advances in community ecology can be adopted to address contemporary challenges in disease research. These analytical tools can identify the factors governing complex assemblages of multiple hosts, parasites, and vectors, and reveal how processes link across scales from individual hosts to regions. They can also determine the drivers of heterogeneities among individuals, species, and regions to aid targeting of control strategies. We provide examples where these principles have enhanced disease management and illustrate how they can be further extended.

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Ebola Cases and Health System Demand in Liberia

Abstract: The authors develop a multi-type branching process model of the 2014 Liberian Ebola outbreak that incorporates the impacts of changes in behavior on potential transmission scenarios, thereby informing the path to containment of the epidemic.

Cited by 71 publications

References 24 publications

Spatial spread of the West Africa Ebola epidemic

Spatial spread of the West Africa Ebola epidemic

Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone

Why infectious disease research needs community ecology

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