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Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.

Valeri, L; Patterson-Lomba, O; Gurmu, Y; Ablorh, A; Bobb, J; Townes, FW; Harling, G; (2016) Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators. PLoS One , 11 (10) , Article e0163544. 10.1371/journal.pone.0163544. Green open access

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Abstract

BACKGROUND: The recent Ebola virus disease (EVD) outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered. METHODS: To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2) in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models. RESULTS: The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic. DISCUSSION: By combining two common methods-estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models-we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.

Type: Article
Title: Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0163544
Publisher version: http://dx.doi.org/10.1371/journal.pone.0163544
Language: English
Additional information: © 2016 Valeri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricte d use, distribu tion, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
URI: https://discovery.ucl.ac.uk/id/eprint/1529358
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