Are influenza surveillance data useful for mapping presentations?

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Public Health Surveillance

Background:

• Geographical Information System (GIS) mapping of influenza data is scarce, with existing data aggregated over large areas.
• Non-morbidity related differences (e.g., consultation behavior, case definition interpretation) limit the use of practice-level morbidity data in GIS mapping.
• Pooling data from multiple practices reduces these confounding factors but results in low geographical resolution.

Purpose of the Study:

• To investigate the harmonization of practice-level morbidity data for improved geographical resolution in influenza mapping.
• To reduce the impact of non-morbidity related differences on the interpretation of influenza data.
• To enable more precise spatial prediction and mapping of influenza activity.

Main Methods:

• Applied different harmonization methods to practice-level data from Germany (acute respiratory infections) and The Netherlands (influenza-like illnesses).
• Harmonized indices between countries by scaling them relative to the peak activity level during a typical influenza epidemic.
• Utilized the Kriging method for spatial prediction of influenza data.

Main Results:

• Demonstrated the possibility of harmonizing practice-level data to reduce confounding differences.
• Achieved harmonization of indices between countries through relative scaling.
• Applied Kriging for spatial prediction, enabling preliminary results for improved influenza mapping.

Conclusions:

• Harmonization of practice-level data is feasible and essential for achieving sufficient geographical resolution in influenza mapping.
• The developed methods show potential for reducing non-morbidity related variations, leading to more accurate spatial representations of influenza.
• Preliminary results suggest that this approach can enhance the utility of GIS in influenza surveillance and public health response.