Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study

Agua-Agum, Junerlyn; Ariyarajah, Archchun; Aylward, Bruce; Bawo, Luke; Bilivogui, Pepe; Blake, Isobel M.; Brennan, Richard J.; Cawthorne, Amy; Cleary, Eilish; Clement, Peter; Conteh, Roland; Cori, Anne; Dafae, Foday; Dahl, Benjamin; Dangou, Jean-Marie; Diallo, Boubacar; Donnelly, Christl A.; Dorigatti, Ilaria; Dye, Christopher; Eckmanns, Tim; Fallah, Mosoka; Ferguson, Neil M.; Fiebig, Lena; Fraser, Christophe; Garske, Tini; Gonzalez, Lice; Hamblion, Esther; Hamid, Nuha; Hersey, Sara; Hinsley, Wes; Jambei, Amara; Jombart, Thibaut; Kargbo, David; Keita, Sakoba; Kinzer, Michael; George, Fred Kuti; Godefroy, Beatrice; Gutierrez, Giovanna; Kannangarage, Niluka; Mills, Harriet L.; Moller, Thomas; Meijers, Sascha; Mohamed, Yasmine; Morgan, Oliver; Nedjati-Gilani, Gemma; Newton, Emily; Nouvellet, Pierre; Nyenswah, Tolbert; Perea, William; Perkins, Devin; Riley, Steven; Rodier, Guenael; Rondy, Marc; Sagrado, Maria; Savulescu, Camelia; Schafer, Ilana J.; Schumacher, Dirk; Seyler, Thomas; Shah, Anita; Van Kerkhove, Maria D.; Wesseh, C. Samford; Yoti, Zabulon

The transmission network can be described by a network of nodes (cases) linked by directed edges (exposures leading to transmission). In order to characterise the heterogeneities in transmission, we analysed the properties of this network. We looked at separate networks for funeral and non-funeral exposures (see section 1.10 in S1 Text). For each, we analysed the outdegree distribution, i.e., the distribution of the number of cases naming the same matched contact, which can be seen as a proxy for the distribution of the number of secondary cases per index case. Several parametric distributions were fitted to each of the two observed out-degree distributions using maximum likelihood estimation (see section 1.10 in S1 Text for a list of the distributions explored). The Akaike information criterion corrected for finite sample sizes (AICc) [17] was then used to select the best distribution. For the best-fitting distribution, 95% confidence intervals for the distribution parameters were obtained using the likelihood ratio test, and the confidence interval for the corresponding distribution was obtained by numerical sampling.