Tsung-Chien (Jonathan) Lu

Influenza-like illness (ILI) has been a major threat to the public health around the world. To inform influenza response by enhancing and supporting disease surveillance, a syndromic surveillance system collects case counts that are aggregated from multiple sources and jurisdictions. Although each jurisdiction has their own planned uses of the data, most systems focus on early detection of the outbreak in regional level response and the algorithms they are using often do not point to a route of transmission. In this work, we seek to develop approaches to aid comparison of data among jurisdictions to improve detection of geographic patterns in disease spread. Using cross-correlation to assess the pairwise similarity between regional case counts, we introduce a cross-correlation network based on ILI activity to reveal potential spatio-temporal patterns in disease transmission. The resulting networks were plotted and visualized in the map with the R statistical package. To evaluate the feasibility and utility of this approach, we validate these networks against population-level variables influencing the spread of infectious disease, including flight passenger volume, census worker flow, and geographic distance. In our analysis, the spatio-temporal transmission of ILI correlated more closely with state-to-state census worker flows and distance between states than with flight passenger flows. We demonstrate how this visualization motif might enhance existing tools used for the purpose of syndromic surveillance. Finally, limitations of the approach, broader implications for disease surveillance and informatics, and future directions for this research will be discussed.