Identifying Influenza Hotspots and Vaccine Uptake Variation

Public health agencies need robust data visualization and analytic tools to understand and respond effectively to infectious diseases. However, traditional epidemiologic surveillance and visualization methods often lack advanced analytics and/or decision support features that enhance response efforts.

The Midwest Analytics and Disease Modeling Center (MADMC) developed a multi-component tool that fills these gaps by incorporating comprehensive statewide electronic health record data into a public health tool with advanced analytic capabilities, including geospatial cluster detection and scenario modeling. The tool was developed and piloted with influenza, but can easily be adapted to other conditions – making it a response-ready tool for novel or emerging diseases.

Our Work

In collaboration with partners at MDH and the Minnesota Electronic Health Records (EHR) Consortium, our team built an analytic tool that identifies influenza hotspots, highlights vaccine disparities, and estimates hospitalization rates under different flu season scenarios. Epidemiologists at MDH piloted and provided feedback on the tool during the 2024-2025 flu season. The tool is now being refined and rolled out to a broader set of public health users at the state and local level to support influenza surveillance and response.

“We can use this tool to supplement our existing surveillance with enhanced looks into geography and demographics. It will fill a large gap in our health equity efforts.” – MDH pilot user feedback

About the Tool

MADMC’s geospatial tool leverages EHR data from 10 Minnesota health systems and includes multiple analytic components:

• Descriptive Epidemiological Analyses
  • Designed and developed by a member of the influenza team at MDH, this component includes visualizations showing disease cases and vaccination trends season-to-season. Users have the ability to view data by characteristic (age, race/ethnicity, social vulnerability index, and geographic area) and by respiratory disease (influenza, RVS, and COVID-19).
• Geospatial Cluster Detection
  • This feature uses SaTScan software to identify hotspots, or clusters of influenza cases, across the state at a census tract level. Clusters can be identified by age group, with additional visualizations and data related to different geographic levels (county, region, and school district), social vulnerability index, and hospitalization rates.
• Vaccine Disparities & Scenario Modeling Decision Support
  • This feature maps vaccine coverage by age at the census tract level and makes it easy for users to identify areas of high vs. low vaccine coverage in their local jurisdiction. Furthermore, scenario modeling allows users to explore how increasing (or decreasing) vaccine coverage in their jurisdiction may impact influenza-related hospitalization rates under different scenarios of flu season severity.

Related Resources

Carlson M, Erickson L, Zirnhelt Z, Chen K, Wheatley M, Berman JD, Searle K, Drawz P, Sanders J, Muscoplat M, McMahon M, Enns E, Winkelman T, Sweet K. Development of a Geospatial Cluster Detection and Decision Support Dashboard. Annual Meeting of the Council of State and Territorial Epidemiologists. Grand Rapids, MI. 2025.