00132 Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach

Reference Article

Adegboye, O., Gayawan, E., James, A., Adegboye, A., & Elfaki, F. (2021). Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach. Zoonoses and public health, 68(5), 443–451. https://doi.org/10.1111/zph.12828

Abstract

Abstract Ebola virus (EBV) disease is globally acknowledged public health emergence, which is endemic in the West and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impart of reported violence of the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) that allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation based on the integrated nested Laplace approximation (INLA). Our findings reveal strong association between violent events in the affected areas and the reported EBV cases and deaths, and the presence of clusters for both cases and deaths both of which spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hotspot identification, location-specific disease surveillance and intervention. Impacts In 2018, the Democratic Republic of Congo (DRC) confirmed their tenth Ebola epidemic in 40 years. The outbreak is the country’s largest Ebola outbreak and the second largest ever recorded after the West African epidemic of 2014-2016. The current outbreak is reported to be occurring in a longstanding conflict zone, this study focused investigating the spatial distribution of Ebola incidence in DRC and the role of violent events. Violent events in the affected areas was found to be significantly associated with reported Ebola cases, which is highly relevant for hotspot identification and location-specific disease surveillance and intervention.