Online sequential monitoring of the disease incidence rates is critically important for public health and stability of our society. Governments around the world have invested a great amount of resource in building efficient disease reporting and surveillance systems. In these systems, conventional control charts, such as the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) charts, are usually included for disease surveillance purpose. However, these charts require many assumptions on the observed data, including the ones of independent and identically normally distributed data when no disease outbreaks are present. These assumptions can hardly be valid in practice, making the results from the conventional control charts unreliable. Motivated by an application to monitor the Florida influenza-like illness data, we develop a new sequential monitoring approach, which can accommodate the dynamic nature of the disease incidence rates, spatio-temporal data correlation, and non-normality. It is shown that our proposed method is much more reliable to use in practice than the commonly used conventional charts for sequential monitoring of disease incidence rates.