This study has created an application which allows users to examine relationships between seasonal environmental changes and infectious diseases.
Combining regression modelling with visualisation techniques, the application provides a tool to interrogate a number of data sources and quickly explore potential relationships.
It has combined Met Office data on climate variables with Public Health England’s infectious disease databases.
The incidence and geographic variation of diseases such as Lyme, salmonella, legionella and campylobacter can be compared with variability in factors such as temperature and precipitation, as well as longer term changes in climate.
Researchers have used this tool to map changing trends in infectious diseases, identifying new and important associations between specific climate variables and disease.
Prior to MEDMI, a lack of linked data prevented the identification of key relationships and limited the potential for early warning and planning in this area.
This project is hoping to improve our ability to forecast future trends in infectious diseases, identify the timing and location of outbreaks, and pinpoint the environmental conditions most likely to spread disease.
Ultimately, the methodology developed here will help authorities prevent outbreaks and plan how they use their resources in high risk areas.