Syndromic surveillance depends on the monitoring of consumer data sources for early warning of unspecified disease outbreaks. These sources include clinical data, such as counts of emergency department visits, and nonclinical data, such as over-the-counter remedy sales. For sensitivity and timeliness at practical alert rates, developers have tried to adapt chart-based methods of statistical process control. Obstacles are evolving, often nonstationary input data streams, target signal uncertainty, and cyclic or seasonal background data behavior. Reliable performance often requires a combination of modeling and process control. This presentation applies generalized exponential smoothing to make forecasts and measures predictive accuracy and the performance of residual-based charts. This approach is compared to conventional methods for sensitivity and robustness using several data types.