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The spatial-temporal pattern of excess influenza visits at the (sub-)urban scale


Quantifying the spatial-temporal diffusion of diseases such as seasonal influenza is difficult at the urban scale for a variety of reasons including the low specificity of the extant data, the heterogenous nature of healthcare seeking behavior and the speed with which diseases spread throughout the city. Nevertheless, the New York City Department of Health and Mental Hygiene’s syndromic surveillance system attempts to detect spatial clusters resulting from outbreaks of influenza. The success of such systems is dependent on there being a discernible spatial-temporal pattern of disease at the neighborhood (sub-urban) scale.

We explore ways to extend global methods such as serfling regression that estimate excess burdens during outbreak periods to characterize these patterns. Traditionally, these methods are aggregated at the national or regional scale and are used only to estimate the total burden of a disease outbreak period. Our extension characterizes the spatial-temporal pattern at the neighborhood scale by day. We then compare our characterizations to prospective spatial cluster detection efforts of our syndromic surveillance system and to demographic covariates.



To develop a novel method to characterize the spatial-temporal pattern of seasonal influenza and then use this characterization to: (1) inform the spatial cluster detection efforts of syndromic surveillance, (2) explore the relationship of spatial-temporal patterns and covariates and (3) inform conclusions made about the burden of seasonal and pandemic influenza. 

Submitted by hparton on