The performance of even the most advanced syndromic surveillance systems can be undermined if the monitored data is delayed before it arrives into the system. In such cases, an outbreak may be detected only after it is too late for appropriate public health response. Surveillance systems can experience delays in data availability for a number of reasons: The process of transmitting data from data sources to the surveillance system can involve delays, especially in large systems where data is first aggregated across a national network of data sources before being transmitted to the surveillance system. Delays can also arise in the course of care, where, for example, a diagnosis is not available for a few days after the healthcare encounter. It is important to minimize delays in data availability in order to maintain timeliness of detection [1]. When this is not possible, it is desirable to compensate for these data delays to minimize their effects.
Objective
This paper describes an approach to improving the detection timeliness of real-time health surveillance systems by modeling and correcting for delays in data availability.