Vector-borne diseases are on the rise globally. As the consequences of climate change are becoming evident, climate-based models of disease risk are of growing importance. Here, we review the current state-of-the-art in both mechanistic and correlative disease modelling, the data driving these models, the vectors and diseases covered, and climate models applied to assess future risk. We find that modelling techniques have advanced considerably, especially in terms of using ensembles of climate models and scenarios. Effects of extreme events, precipitation regimes, and seasonality on diseases are still poorly studied. Thorough validation of models is still a challenge and is complicated by a lack of field and laboratory data. On a larger scale, the main challenges today lie in cross-disciplinary and cross-sectoral transfer of data and methods.

Highlights

The use of ensembles of different climate models for future projections, as well as multiple different mechanistic or correlative disease models per study, is increasing.

Communicating uncertainties related to disease models, different climate models, and emission and population pathways to end users is becoming a common thing to do.

Most models tend to project an increased risk for vector-borne disease (VBD) transmission at high latitudes and elevations during the upcoming century.

While mechanistic models typically cover the whole chain of infection by default, most environmental niche models (ENMs) still focus on vector distributions alone; they are increasingly applied to whole disease systems as well.