Managing uncertainty in early estimation of epidemic behaviors using scenario trees

The onset of an epidemic can be foreshadowed theoretically through observation of a number of syndromic signals, such as absenteeism or rising sales of particular pharmaceuticals. The success of such approaches depends on how well the uncertainty associated with the early stages of an epidemic can be managed. This article uses scenario trees to summarize the uncertainty in the parameters defining an epidemiological process and the future path the epidemic might take. Extensive simulations are used to generate various syndromic and epidemic time series, which are then summarized in scenario trees, creating a simple data structure that can be explored quickly at surveillance time without the need to fit models. Decisions can be made based on the subset of the uncertainty (the subtree) that best fits the current observed syndromic signals. Simulations are performed to investigate how well an underlying dynamic model of an epidemic with inhomogeneous mixing and noise fluctuations can capture the effects of social interactions. Two noise terms are introduced to capture the observable fluctuations in the social network connectivity and variation in some model parameters (e.g., infectious time). Finally, it is shown how the entire framework can be used to compare syndromic surveillance systems against each other; to evaluate the effect of lag and noise on accuracy; and to evaluate the impact that differences in syndromic behavior among susceptible and infected populations have on accuracy.