Technical Results

Three work packages (AS1) were executed. In AP1, the need for data adaptation was identified and a concept for aligning SurvNet and ARS with PIK’s climate station data and modeling methods was developed. The concept was fully documented and approved.

AP2 implemented the actual modifications. SurvNet data were restructured to include district‑level and weekly time stamps, enabling integration with climate variables. For ARS, coverage analysis revealed that only a limited number of hospitals contributed continuous data over seven years, rendering the dataset insufficient for robust modeling. Consequently, a shorter time window with more hospitals was tested but proved too brief for the planned analyses. Despite these limitations, the modified SurvNet data for Campylobacter, MRSA, and Legionella were successfully extracted and prepared for modeling.

AP3 performed statistical analyses on the adapted datasets. Correlation studies identified a clear summer peak for Campylobacter and Salmonella in temperate regions, with temperature differences exceeding 30 K between winter and summer months correlating with higher infection rates. MRSA data from SurvNet showed no discernible seasonality, while preliminary Legionella analyses suggested a potential seasonal trend. Epidemiological interpretation linked these patterns to climate‑driven changes in pathogen prevalence and highlighted the risk of a 2 K increase in Germany’s mean annual temperature by mid‑century, which could shift the spectrum of circulating pathogens.

Collaboration and Project Structure

The project is a joint effort between the RKI (FG35 and FG37), the Charité – Universitätsmedizin Berlin’s Institute for Hygiene and Environmental Medicine, and PIK. The RKI provided long‑standing expertise in surveillance data collection and analysis, while Charité contributed clinical epidemiology and data management. PIK supplied climate data, modeling expertise, and scenario development. The consortium operated under the CLIP-ID umbrella, coordinating data sharing, methodological alignment, and joint publication efforts. Funding was allocated through the German Federal Ministry of Education and Research (BMBF) under grant number 03ZZ0807C. The project’s 30‑month timeline included a planned but ultimately unapproved extension for Legionella data analysis. Overall, the collaboration successfully integrated epidemiological surveillance with climate science, producing a framework that can be applied to future studies on climate‑driven infectious disease dynamics.