Online sequential monitoring of spatio-temporal disease incidence rates

Online sequential monitoring of the incidence rates of chronic or infectious diseases is critically important for public health. Governments have invested a great amount of money in building global, national and regional disease reporting and surveillance systems. In these systems, conventional control charts, such as the cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) charts, are usually included for disease surveillance purposes. However, these charts require many assumptions on the observed data, including the ones that the observed data should be independent at different places and/or times, and they should follow a parametric distribution when no disease outbreaks are present. These assumptions are rarely 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 in this article, which can accommodate the dynamic nature of the observed disease incidence rates (i.e., the distribution of the observed disease incidence rates can change over time due to seasonality and other reasons), spatio-temporal data correlation, and arbitrary data distribution. It is shown that the new method is more reliable to use in practice than the commonly used conventional charts for sequential monitoring of disease incidence rates. Because of its generality, the proposed method should be useful for many other applications as well, including spatio-temporal monitoring of the air quality in a region or the sea-level pressure data collected in a region of an ocean.