Spatial and Temporal Dynamics of Influenza

Despite the significant amount of research conducted on the epidemiology of seasonal influenza, the patterns in the annual oscillations of influenza epidemics have not been fully described or understood. Furthermore, the current understanding of the intrinsic properties of influenza epidemics is limited by the geographic scales used to evaluate the data. Analyses conducted at larger spatial scales may potentially conceal local trends in disease structure which may reveal the effect of population structure or environmental factors on disease spread. By using influenza incidence data from the Commonwealth of Pennsylvania and United States influenza mortality data, this dissertation characterizes seasonal influenza epidemics, evaluates factors that drive local influenza epidemics, and provides an initial assessment in how administrative borders influence surveillance for local and regional influenza epidemics.Evidence of spatial heterogeneity existed in the distribution of influenza epidemics for Pennsylvania counties resulting in a cluster of elevated incidence in the South Central region of the state that persisted during the entire study period (2003-2009). Lower monthly precipitation levels during the influenza season (OR = 0.52, p = 0.0319), fewer residents over age 64 (OR = 0.27, p = 0.01) and fewer residents with more than a high school education (OR = 0.76, p = 0.0148) were significantly associated with membership in this cluster. In addition, significant synchrony in the timing of epidemics existed across the entire state and decayed with distance (regional correlation r = 62%). Synchrony as a function of population size displayed evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations was the best predictor of influenza spread suggesting that non-routine and leisure travel drive local epidemics. Within the United States, clusters of epidemic synchronization existed, most notably in densely populated regions where connectivity is stronger. Observation of county and state epidemic clusters highlights the importance and necessity of correctly identifying the ontologic unit of epidemicity for influenza and other diseases. Recognition of the appropriate geographic unit to implement effective surveillance and prevention methods can strengthen the public health response and minimize inefficient mechanisms