We present a simple, fast, and easily interpretable procedure that results in faster detection of outbreaks in multiple spatial regions. Disease counts from neighboring regions are aggregated to compute a Poisson CUSUM statistic for each region. Instead of controlling the average run length error criterion in the testing process, we instead utilize the false discovery rate. Additionally, p-values are used to make decisions instead of traditional critical-values. The use of the false discovery rate and p-values in testing allows us to utilize more powerful multiple testing methodologies. The procedure is successfully applied to detect the 2011 Salmonella Newport outbreak in Germany.