Coxiella burnetii, an intracellular zoonotic bacterium, affectsing various livestock and wildlife species and poses significant risks to human health. This study aims to assess how climate change could impact the global distribution and habitat suitability of Coxiella burnetii, the pathogen responsible for Q fever.

An ensemble species distribution modelling approach, integrating regression-based and machine-learning algorithms (GLM, GBM, RF, MaxEnt), was used to project habitat suitability (Current time and by 2050, 2070, and 2090). Climate variables were obtained from five global circulation models (GCMs) under two climate change scenarios (SSP2-4.5 and SSP5-8.5). The study evaluated the models’ performance using the area under the curve (AUC) and true skill statistics (TSS).

Results show that under current climate conditions, C. burnetii is widespread across regions like North and South America, Europe, and parts of Africa, Asia, and Australia. Future projections indicate a northward shift in habitat suitability, especially under the severe SSP5-8.5 scenario, with significant expansions into Russia, northern Europe, and Canada. Conversely, regions in South America, Africa, and Australia may see declines in suitable habitats. By 2090, a 44.56 % (range: 33–57.9 %) across the models, increase in suitable habitat is predicted, accompanied by a 27.66 % (range: 22.4–31.7 %) loss of current habitats.

Findings indicate that temperature seasonality and precipitation of the driest month are the most influential climatic variables shaping the distribution of C. burnetii. These results underscore the importance of climate variability in influencing the pathogen's global distribution and highlight the critical role of environmental factors in predicting future habitat shifts.

The study highlights the profound impact climate change could have on the global distribution of C. burnetii. It underscores the need for proactive public health strategies in emerging high-risk areas and emphasizes the importance of mitigating risks in regions experiencing habitat declines. These findings offer valuable insights for public health planning and livestock management under future climate scenarios. In interpreting these results, it is important to consider modelling uncertainties, including assumptions and data limitations.