Transmission network of the 2014–2015 Ebola epidemic in Sierra Leone

Abstract

WY, 0000-0002-7555-9728Understanding 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 ¼ 0.76, p ¼ 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.

Spatial spread of the West Africa Ebola epidemic

“…Finally, there may be some bias in the movement data that results from socio-economic differences between infected patients and the sample of cell phone data records, although we consider this less likely. Yang et al [18] had similar success with the approach we used here, directly using distance and population size instead of trying to estimate interlocale commuter flows.…”

“…Particularly, the probability of transmission between locations depended on distance, population density and international border closures between Guinea, Liberia and Sierra Leone and neighbouring countries. Other studies at more local scales [10,18] support the suitability of generalized gravity models to match observed patterns in the Ebola outbreak, but were not constructed to provide the contrasts studied here. Thus, we additionally found lower support for a variety of other hypothesized patterns of spread, including some proxies for human mobility estimated from individual cell phone records [11,21] which here were a poor predictor of spatial spread.…”

“…Because this model does not explicitly include information on the number of cases in administrative units [18] or other factors like treatment facilities [39], any discernible effects of these factors are instead included in the estimated effects of distance and population density. This may be a potential advantage of aggregated models for predicting spread and allocating resources, given probable differences in under-reporting over time and among countries.…”

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City

“…granularity) of simulation does inclusion of spatial connection improve forecast performance; and (2) whether forecast performance differs between interpandemic and pandemic seasons given the differing transmissibility of influenza strains. To address these questions, we build a forecast system using a patch network epidemic model [14], in conjunction with the ensemble adjustment Kalman filter (EAKF) [15]. The patch network model incorporates spatial connectivity among subpopulations, and each subpopulation is described by a compartmental model.…”

Use of the Filovirus Animal Non-Clinical Group (FANG) Ebola virus immuno-assay requires fewer study participants to power a study than the Alpha Diagnostic International assay

“…Persistent genomic virus RNA signals have been detected in EVD survivors over a year after resolution of both clinical disease and viremia(Crozier, 2016; Deen et al, 2017; Uyeki et al, 2016). As a result, sexual and vertical transmission of the virus may occur, as evidenced by at least 3 EVD clusters in Liberia, Sierra Leone, and Guinea (Arias et al, 2016; Blackley et al, 2016;Fischer et al, 2016; Fischer and Wohl, 2016; Mate et al, 2015; Sow et al, 2016; Yang et al, 2015). Though the outbreak has ended, countermeasures should be developed to combat any potential reintroductions of EVD into the population by these or other traditional transmission routes.…”