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PLoS One. 2016 Oct 12;11(10):e0163544. doi: 10.1371/journal.pone.0163544. eCollection 2016.

Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.

Author information

1
Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, United States of America.
2
Harvard Medical School, Boston, United States of America.
3
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States of America.
4
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States of America.
5
Group Health Research Institute, Seattle, United States of America.
6
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, United States of America.

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.

PMID:
27732614
PMCID:
PMC5061396
DOI:
10.1371/journal.pone.0163544
[Indexed for MEDLINE]
Free PMC Article

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