An application of a mathematical model to adjust for time lag in case reporting

In a dynamic, fluctuating surveillance system, the time lag of case reporting often causes an artificial plateau in an epidemic curve. Arbitrarily ignoring data reported in the most recent period to avoid this bias causes the loss of valuable information. In this report, we propose an application of a mathematical model to adjust for the underreporting bias owing to the time lag of the reporting process. We present an example using the acquired immunodeficiency syndrome incidence data for the homosexual group in the San Francisco surveillance system to illustrate and evaluate prospectively this proposed technique. The results show that the adjusted incidence obtained with the model agrees reasonably well with the true incidence, except for the last month of the period under consideration.