title: Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone.
abstract: Understanding the growth and spatial expansion of (re)emerging infectious disease outbreaks, such as Ebola and avian influenza, is critical for the effective planning of control measures; however, such efforts are often compromised by data insufficiencies and observational errors. Here, we develop a spatial-temporal inference methodology using a modified network model in conjunction with the ensemble adjustment Kalman filter, a Bayesian inference method equipped to handle observational errors. The combined method is capable of revealing the spatial-temporal progression of infectious disease, while requiring only limited, readily compiled data. We use this method to reconstruct the transmission network of the 2014-2015 Ebola epidemic in Sierra Leone and identify source and sink regions. Our inference suggests that, in Sierra Leone, transmission within the network introduced Ebola to neighbouring districts and initiated self-sustaining local epidemics; two of the more populous and connected districts, Kenema and Port Loko, facilitated two independent transmission pathways. Epidemic intensity differed by district, was highly correlated with population size (r = 0.76, p = 0.0015) and a critical window of opportunity for containing local Ebola epidemics at the source (ca one month) existed. This novel methodology can be used to help identify and contain the spatial expansion of future (re)emerging infectious disease outbreaks.
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA wy2202@columbia.edu.
authors.forename: Wan
authors.identifier: http://orcid.org/0000-0002-7555-9728
authors.initials: W
authors.lastname: Yang
authors.affiliation: China Mobile Laboratory Response Team for Ebola in Sierra Leone, Freetown, Sierra Leone.
authors.forename: Wenyi
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Ministry of Health and Sanitation, Freetown, Sierra Leone.
authors.forename: David
authors.initials: D
authors.lastname: Kargbo
authors.affiliation: China Mobile Laboratory Response Team for Ebola in Sierra Leone, Freetown, Sierra Leone.
authors.forename: Ruifu
authors.initials: R
authors.lastname: Yang
authors.affiliation: China Mobile Laboratory Response Team for Ebola in Sierra Leone, Freetown, Sierra Leone.
authors.forename: Yong
authors.initials: Y
authors.lastname: Chen
authors.affiliation: China Mobile Laboratory Response Team for Ebola in Sierra Leone, Freetown, Sierra Leone.
authors.forename: Zeliang
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Ministry of Health and Sanitation, Freetown, Sierra Leone.
authors.forename: Abdul
authors.initials: A
authors.lastname: Kamara
authors.affiliation: Ministry of Health and Sanitation, Freetown, Sierra Leone.
authors.forename: Brima
authors.initials: B
authors.lastname: Kargbo
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
authors.forename: Sasikiran
authors.initials: S
authors.lastname: Kandula
authors.affiliation: Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80305, USA.
authors.forename: Alicia
authors.initials: A
authors.lastname: Karspeck
authors.affiliation: China Mobile Laboratory Response Team for Ebola in Sierra Leone, Freetown, Sierra Leone liuchao9588@sina.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Liu
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA jls106@columbia.edu.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Shaman
mesh_terms: D000066949:Biobehavioral Sciences; D005260:Female; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology* / Q000635:transmission*; D006801:Humans; D008297:Male; D012807:Sierra Leone / Q000453:epidemiology
keywords: Bayesian inference; Ebola; ensemble adjustment Kalman filter; gravity model; transmission network