We used Amazon's EC2 (Elastic Compute Cloud) services to experiment with cloud computing for syndromic surveillance. We applied two cloud service models: Infrastructure as a Service (IaaS) and Software as a Service (SaaS). Our first goal was to apply cloud computing technologies in order to reduce computational time needed for syndromic analyses. Scan statistics, due to their reliance on Monte Carlo simulation to find confidence levels, are particularly well suited to being improved by parallel computation. We used the R package DCluster to calculate scan statistics and combined that with the SNOW (Simple Network of Workstations) package. We also experimented with using cloud computing to parallelize the AMOEBA approach to cluster detection.