Zoonotic leishmaniasis, caused by Leishmania infantum and transmitted by phlebotomine sand flies, remains endemic in the Mediterranean basin, where dogs are the main domestic reservoirs for human infection. In the Iberian Peninsula, canine leishmaniasis (CanL) seroprevalence and incidence vary across space and time, reflecting heterogeneous climatic, ecological and socio-environmental conditions. Ongoing climate change, environmental degradation, and increasing mobility of humans and animals are expected to expand vector distribution and intensify transmission risk. The Case Study 1 (CS1) of the Horizon Europe project PLANET4HEALTH aims to develop, refine and evaluate a data-driven Early Warning System (EWS) for CanL in the Iberian Peninsula, integrating environmental, climatic, hydrometeorological, entomological, animal and human health data under a One Health perspective.
CS1 follows a transdisciplinary study design combining retrospective data integration with prospective stakeholder engagement. Historical and near real-time hydrometeorological indicators (e.g., temperature, humidity, rainfall, soil moisture) and climatic variables will be integrated with sand fly surveillance, veterinary CanL diagnosis data, including serological Leishmania diagnostic results and CanL case records, and domestic dog population data. Statistical and machine learning models will be used to characterise vector seasonality, estimate CanL risk and generate spatially explicit short-term forecasts and future projections. Sand fly surveillance, dog and human health data, or canine disease proxy data are sourced from previous and ongoing national and European sand fly surveillance efforts, veterinary clinical and diagnostic databases from Portugal and Spain, and national public health surveillance systems, under formal data sharing agreements. Model outputs will be iteratively evaluated with veterinarians, public health authorities and other end-users through questionnaires, online feedback and co-creation workshops, to assess perceived usefulness, feasibility, and usability of the EWS.
This framework demonstrates the translation of comprehensive multi-source One Health datasets into evidence-based outputs integrated into the PLANET4HEALTH EWS, establishing an innovative approach to preparedness and mitigation of CanL. Inclusion of iterative end-user evaluation guarantees operational relevance and practical utility for stakeholders. This approach is expected to optimise proactive risk management, while offering a scalable model to other endemic regions.
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