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

Ebola virus (EBV) disease is a globally acknowledged public health emergency, endemic in the west and equatorial Africa. To understand epidemiology especially dynamic pattern of EBV disease, we analyse case notification data for confirmed cases reported deaths ongoing outbreak Democratic Republic Congo (DRC) between 2018 2019, examined impact violence on spread virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) allows us to quantify spatial patterns at every point domain. Parameter estimation was based integrated nested Laplace approximation (INLA). Our findings revealed positive association violent events affected areas (posterior mean = 0.024, 95% CI: 0.005, 0.045) 0.022, 0.041). Translating an increase 2.4% 2.2% relative risks associated with unit (one additional average 45 events). We also observed clusters neighbouring locations similar manners. Findings from study are therefore useful hot spot identification, location-specific surveillance intervention.