Dataset Information

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.

SUBMITTER: Yang W

PROVIDER: S-EPMC4685836 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone.

Yang Wan W Zhang Wenyi W Kargbo David D Yang Ruifu R Chen Yong Y Chen Zeliang Z Kamara Abdul A Kargbo Brima B Kandula Sasikiran S Karspeck Alicia A Liu Chao C Shaman Jeffrey J

Journal of the Royal Society, Interface 20151101 112

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. T ...[more]

PMID: 26559683

Similar Datasets

Project description:BackgroundPerceived susceptibility to a disease threat (risk perception) can influence protective behaviour. This study aims to determine how exposure to information sources, knowledge and behaviours potentially influenced risk perceptions during the 2014-2015 Ebola Virus Disease outbreak in Sierra Leone.MethodsThe study is based on three cross-sectional, national surveys (August 2014, n = 1413; October 2014, n = 2086; December 2014, n = 3540) that measured Ebola-related knowledge, attitudes, and practices in Sierra Leone. Data were pooled and composite variables were created for knowledge, misconceptions and three Ebola-specific behaviours. Risk perception was measured using a Likert-item and dichotomised into 'no risk perception' and 'some risk perception'. Exposure to five information sources was dichotomised into a binary variable for exposed and unexposed. Multilevel logistic regression models were fitted to examine various associations.ResultsExposure to new media (e.g. internet) and community-level information sources (e.g. religious leaders) were positively associated with expressing risk perception. Ebola-specific knowledge and hand washing were positively associated with expressing risk perception (Adjusted OR [AOR] 1.4, 95% Confidence Interval [CI] 1.2-1.8 and AOR 1.4, 95% CI 1.1-1.7 respectively), whereas misconceptions and avoiding burials were negatively associated with risk perception, (AOR 0.7, 95% CI 0.6-0.8 and AOR 0.8, 95% CI 06-1.0, respectively).ConclusionsOur results illustrate the complexity of how individuals perceived their Ebola acquisition risk based on the way they received information, what they knew about Ebola, and actions they took to protect themselves. Community-level information sources may help to align the public's perceived risk with their actual epidemiological risk. As part of global health security efforts, increased investments are needed for community-level engagements that allow for two-way communication during health emergencies.

Dataset Information

Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone.

Publications

Transmission network of the 2014-2015 Ebola epidemic in Sierra Leone.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets