Seasonality Assessment for Biosurveillance Systems

Biosurveillance systems for infectious diseases typically deal with nonlinear time series. This nonlinearity is due to the non-Gaussian and nonstationary nature of an outcome process. Infectious diseases (ID), waterborne and foodborne enteric infections in particular, are typically characterized by a sequence of sudden outbreaks, which are often followed by long low endemic levels. Multiple outbreaks occurring within a relatively short time interval form a seasonal pattern typical for a specific pathogen in a given population. Seasonal variability in the probability of exposure combined with a partial immunity to a pathogen adds to the complexity of seasonal patterns. Although seasonal variation is a well-known phenomenon in the epidemiology of enteric infections, simple analytical tools for examination, evaluation, and comparison of seasonal patterns are limited. This obstacle also limits analysis of factors associated with seasonal variations. The objectives of this paper are to outline the notion of seasonality, to define characteristics of seasonality, and to demonstrate tools for assessing seasonal patterns and the effects of environmental factors on such patterns. To demonstrate these techniques, we conducted a comparative study of seasonality in Salmonella cases as reported by the state surveillance system in relation to seasonality in ambient temperature, and found that the incidence in Salmonella infection peaked two weeks after a peak in temperature. The results suggest that ambient temperature can be a potential predictor of Salmonella infections at a seasonal scale.