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.

翻译：本研究提出一种时空双贝叶斯模型，利用来自武装冲突地点与事件数据（ACLED）项目的埃塞俄比亚事件级数据（1997-2024），同时分析冲突发生及其死亡人数。死亡人数被处理为两个关联结果：死亡发生的二元变量及发生时的死亡计数。该模型结合了协变量的加性固定效应与捕捉时空影响的随机效应，允许结果特异性效应。协变量包括事件类型和季节作为分类变量，与城市及边境的距离作为非线性效应，人口作为计数模型的偏移项。一个潜在的时空过程解释了共享的空间与时间依赖性，其空间结构通过Matérn场先验建模，并采用集成嵌套拉普拉斯近似（INLA）进行推断。结果显示死亡风险存在强烈的空间聚集性和时间变异性，强调了同时建模两个维度对于更好理解和预测的重要性。空袭、炮击和袭击表现出最高的死亡可能性和计数，而社区冲突和叛乱行为者造成的死亡最多。夏季更易发生多起死亡事件，靠近边境地区驱动了高强度暴力，而远离城市中心则与低强度事件相关。这些结果为规划、政策和资源分配以保护脆弱社区提供了参考依据。