Spatiotemporal Dynamics of Conflict Occurrence and Fatalities in Ethiopia: A Bayesian Model and Predictive Insights Using Event-level Data (1997--2024)

Abstract

This study presents a spatiotemporal dual Bayesian model that examines both the occurrence and number of conflict fatalities using event-level data from Ethiopia (1997-2024), sourced from the Armed Conflict Location and Event Data (ACLED) project. Fatalities are treated as two linked outcomes: the binary occurrence of deaths and the count of deaths when they occur. The model combines additive fixed effects for covariates with random effects capturing spatiotemporal influences, allowing for outcome-specific effects. Covariates include event type and season as categorical variables, proximity to cities and borders as nonlinear effects, and population as an offset term in the count model. A latent spatiotemporal process accounts for shared spatial and temporal dependence, with the spatial structure modeled using a Mat\'ern field prior and inference via Integrated Nested Laplace Approximation (INLA). Results show strong spatial clustering and temporal variation in fatality risk, emphasizing the importance of modeling both dimensions for better understanding and prediction. Airstrikes, shelling, and attacks show the highest fatality likelihood and counts, while communal and rebel actors cause the most deaths. Multiple fatalities are more likely in summer, and proximity to borders drives intense violence, whereas remoteness from urban centers is linked to lower-intensity events. These results provide insight for planning, policy, and resource allocation to protect vulnerable communities.