About: The Ebola virus disease (EVD) epidemic in West Africa in 2013–2015 spread heterogeneously across the three hardest-hit countries Guinea, Liberia and Sierra Leone and the estimation of national transmission of EVD provides little information about local dynamics. To investigate district-level transmissibility of EVD, we applied a statistical modelling approach to estimate the basic reproduction number (R(0)) for each affected district and each country using weekly incident case numbers. We estimated growth rates during the early exponential phase of the outbreak using exponential regression of the case counts on the first eight weeks since onset. To take into account the heterogeneity between and within countries, we fitted a mixed effects model and calculated R(0) based on the predicted individual growth rates and the reported serial interval distribution. At district level, R(0) ranged from 0.36 (Dubréka) to 1.72 (Beyla) in Guinea, from 0.53 (Maryland) to 3.37 (Margibi) in Liberia and from 1.14 (Koinadugu) to 2.73 (Western Rural) in Sierra Leone. At national level, we estimated an R(0) of 0.97 (95% CI 0.77–1.18) for Guinea, 1.26 (95% CI 0.98–1.55) for Liberia and 1.66 (95% CI 1.32–2.00) for Sierra Leone. Socio-demographic variables related to urbanisation such as high population density and high wealth index were found positively associated with R(0) suggesting that the consequences of fast urban growth in West Africa may have contributed to the increased spread of EVD.   Goto Sponge  NotDistinct  Permalink

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  • The Ebola virus disease (EVD) epidemic in West Africa in 2013–2015 spread heterogeneously across the three hardest-hit countries Guinea, Liberia and Sierra Leone and the estimation of national transmission of EVD provides little information about local dynamics. To investigate district-level transmissibility of EVD, we applied a statistical modelling approach to estimate the basic reproduction number (R(0)) for each affected district and each country using weekly incident case numbers. We estimated growth rates during the early exponential phase of the outbreak using exponential regression of the case counts on the first eight weeks since onset. To take into account the heterogeneity between and within countries, we fitted a mixed effects model and calculated R(0) based on the predicted individual growth rates and the reported serial interval distribution. At district level, R(0) ranged from 0.36 (Dubréka) to 1.72 (Beyla) in Guinea, from 0.53 (Maryland) to 3.37 (Margibi) in Liberia and from 1.14 (Koinadugu) to 2.73 (Western Rural) in Sierra Leone. At national level, we estimated an R(0) of 0.97 (95% CI 0.77–1.18) for Guinea, 1.26 (95% CI 0.98–1.55) for Liberia and 1.66 (95% CI 1.32–2.00) for Sierra Leone. Socio-demographic variables related to urbanisation such as high population density and high wealth index were found positively associated with R(0) suggesting that the consequences of fast urban growth in West Africa may have contributed to the increased spread of EVD.
Subject
  • 2014 health disasters
  • Margibi County
  • Climate change mitigation
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