The COVID-19 pandemic presented a forecasting challenge because it disrupted historical patterns, making traditional models unreliable. Demand recovery was asynchronous and geographically uneven, with varying timelines for vaccine rollouts and border reopenings. Waiting for local data in each recovering market would have meant forecasting blind for extended periods. Airbnb's solution was to look sideways across geographies instead of just backward in time.They observed that recovery unfolded sequentially, with some corridors experiencing changes before others. A key signal was the mean booking lead time, which compressed during disruptions and lengthened during recovery. By comparing the timing of these lead time shifts between regions like Europe and North America, they could infer how one market would likely respond to reopening based on the experience of another. This allowed them to use the posterior from an early-affected corridor as an informative prior for a later-affected one.This prior propagation mechanism enabled real-time learning and forecasting, even in markets with scarce local data. The approach required a global footprint with granular data and a hierarchical Bayesian modeling framework. This methodology is applicable beyond crises, such as for predicting the impact of new product feature rollouts or regulatory changes. It allows for immediate learning from early-adopting markets to inform forecasts for those yet to experience the change.