Melioidosis Beyond the Tropics: Environmental Persistence, Climate-Sensitive Risk and Emerging One Health Challenges

Background/Objectives: Melioidosis is an environmentally acquired infection caused by Burkholderia pseudomallei (B. pseudomallei). Although historically framed as a tropical disease, evidence indicates that recognized risk can extend beyond classical endemic regions. This narrative review synthesized Digital Object Identifier (DOI)-verified evidence on environmental persistence, climate-sensitive risk, geographic emergence, and One Health preparedness. Methods: Structured narrative searches of PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Europe PubMed Central (Europe PMC), Crossref, and publisher records were conducted for literature available up to 19 June 2026. Forty-four DOI-verified sources were retained. Evidence categories were derived inductively by inferential function during thematic synthesis and used as a qualitative interpretive framework, not as a validated quantitative risk score. Results: B. pseudomallei persists in soil and water, survives nutrient limitation, and clusters in environmental microfoci, but the interpretive value of detection depends on viability, exposure context, and diagnostic endpoint. Rainfall, humidity, flooding, and cyclones are associated with incidence, severity, or mobilization in several settings, supporting climate-sensitive risk rather than uniform geographic spread. Case-based evidence is strongest when it separates importation, local acquisition, environmental establishment, source attribution, and animal sentinel signals. Human risk depends on exposure route, host susceptibility, diagnostic recognition, and access to prolonged antimicrobial management, whereas animal evidence is best interpreted as sentinel or common-exposure evidence unless reservoir or direct-transmission data are available. Conclusions: Melioidosis beyond the tropics requires graded evidence interpretation because environmental detection, modeled suitability, animal signals, and human cases support different levels of geographic and One Health inference; this approach links early signals to surveillance while reserving higher-confidence claims for convergent evidence.