Understanding the short-run dynamics of conflict and forced displacement is crucial for the design of effective policy responses, yet quantitative analyses in this realm are sparse. This is primarily due to the scarcity of high-frequency displacement data and methodological challenges arising when modeling imperfect data collected in conflict zones. Addressing both issues, we develop a Bayesian panel regression model to assess the short-term impact of conflict on displacement in Somalia. We utilize weekly panel data that encompasses 8 million displacements and 19,000 conflict events from 2017 to 2023. Results suggest a rapid and non-linear displacement response post-conflict, with significant heterogeneity in effects dependent on the nature of conflict events. Our model outperforms standard benchmarks in a displacement forecasting exercise, underscoring its potential for informing decision-makers in crisis scenarios.