Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.

A major outbreak of the Ebola virus occurred in 2014 in Sierra Leone. We investigate the spatial heterogeneity of the outbreak among districts in Sierra Leone. The stochastic discrete-time susceptible-exposed-infectious-removed (SEIR) model is used, allowing for probabilistic movements from one comp...

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Main Authors: Rachid Muleia, Marc Aerts, Christel Faes
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0250765
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spelling doaj-3275a668d438409eaa2bb59245b39fe22021-05-29T04:31:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01165e025076510.1371/journal.pone.0250765Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.Rachid MuleiaMarc AertsChristel FaesA major outbreak of the Ebola virus occurred in 2014 in Sierra Leone. We investigate the spatial heterogeneity of the outbreak among districts in Sierra Leone. The stochastic discrete-time susceptible-exposed-infectious-removed (SEIR) model is used, allowing for probabilistic movements from one compartment to another. Our model accounts for heterogeneity among districts by making use of a hierarchical approach. The transmission rates are considered time-varying. It is investigated whether or not incubation period, infectious period and transmission rates are different among districts. Estimation is done using the Bayesian formalism. The posterior estimates of the effective reproductive number were substantially different across the districts, with pronounced variability in districts with few cases of Ebola. The posterior estimates of the reproductive number at the district level varied between below 1.0 and 4.5, whereas at nationwide level it varied between below 1.0 and 2.5. The posterior estimate of the effective reproductive number reached a value below 1.0 around December. In some districts, the effective reproductive number pointed out for the persistence of the outbreak or for a likely resurgence of new cases of Ebola virus disease (EVD). The posterior estimates have shown to be highly sensitive to prior elicitation, mainly the incubation period and infectious period.https://doi.org/10.1371/journal.pone.0250765
collection DOAJ
language English
format Article
sources DOAJ
author Rachid Muleia
Marc Aerts
Christel Faes
spellingShingle Rachid Muleia
Marc Aerts
Christel Faes
Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
PLoS ONE
author_facet Rachid Muleia
Marc Aerts
Christel Faes
author_sort Rachid Muleia
title Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
title_short Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
title_full Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
title_fullStr Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
title_full_unstemmed Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.
title_sort multi-population stochastic modeling of ebola in sierra leone: investigation of spatial heterogeneity.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description A major outbreak of the Ebola virus occurred in 2014 in Sierra Leone. We investigate the spatial heterogeneity of the outbreak among districts in Sierra Leone. The stochastic discrete-time susceptible-exposed-infectious-removed (SEIR) model is used, allowing for probabilistic movements from one compartment to another. Our model accounts for heterogeneity among districts by making use of a hierarchical approach. The transmission rates are considered time-varying. It is investigated whether or not incubation period, infectious period and transmission rates are different among districts. Estimation is done using the Bayesian formalism. The posterior estimates of the effective reproductive number were substantially different across the districts, with pronounced variability in districts with few cases of Ebola. The posterior estimates of the reproductive number at the district level varied between below 1.0 and 4.5, whereas at nationwide level it varied between below 1.0 and 2.5. The posterior estimate of the effective reproductive number reached a value below 1.0 around December. In some districts, the effective reproductive number pointed out for the persistence of the outbreak or for a likely resurgence of new cases of Ebola virus disease (EVD). The posterior estimates have shown to be highly sensitive to prior elicitation, mainly the incubation period and infectious period.
url https://doi.org/10.1371/journal.pone.0250765
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