Operationalizing One Health Surveillance for Tropical Fungal Diseases: Practical Implications of Bioinformatics for Clinical and Epidemiological Practice

Fungal infections are an increasing global public health concern yet remain underprioritized because of persistent challenges in diagnosis, treatment, and surveillance. The WHO Fungal Priority Pathogens List underscores the growing burden of antifungal resistance and includes agents responsible for neglected tropical mycoses, such as Paracoccidioides spp. and eumycetoma. Despite recent advances, fungal diagnostics remain limited by poor standardization, high costs, and restricted applicability in endemic, resource-limited settings. In addition, mounting evidence shows that fungal pathogens evolve within interconnected human, animal, and environmental contexts. In this perspective, we propose that integrating bioinformatics-driven analytical frameworks within a One Health paradigm can help transform fungal surveillance of tropical fungal diseases from fragmented, reactive systems into coordinated, predictive systems capable of anticipating pathogen emergence and antifungal resistance dynamics. Recent applications of deep learning for mycetoma diagnosis and long-read sequencing for Paracoccidioides genomics illustrate the feasibility and public health relevance of this approach.