One World, One Health: Combatting the Spread of Multidrug-Resistant Pathogens at the Human–Animal–Environment Interface

A special issue of Veterinary Sciences (ISSN 2306-7381).

Deadline for manuscript submissions: 25 May 2026 | Viewed by 1072

Special Issue Editor

Department of Veterinary Medicine, College of Agriculture and Veterinary Medicine, United Arab of Emirates University, Al Ain 52571, United Arab Emirates
Interests: Campylobacter; Salmonella; one health; antimicrobial resistance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The "One World, One Health" approach recognizes that human, animal, and environmental health are deeply interconnected, particularly in the fight against multidrug-resistant (MDR) pathogens. The rise in antimicrobial resistance (AMR)—fueled by antibiotic overuse in healthcare, agriculture, and aquaculture—poses a grave global threat. Resistant bacteria spread across species through contaminated food, water, soil, and direct contact, undermining modern medicine and increasing mortality rates. By prioritizing cross-sector collaboration, sustainable practices, and public awareness, the One Health framework offers a path to curb AMR’s spread, protect vulnerable populations, and preserve antimicrobial efficacy for future generations. This Special Issue provides an opportunity to exchange research results, expertise, and opinions on the following topics:

  • Surveillance and epidemiology of AMR in animals, humans, foods, and the environment, including integrated One Health monitoring systems.
  • Molecular characterization of multidrug-resistant pathogens at the human–animal–environment interface (e.g., WGS, resistome, and mobilome analysis).
  • Pathways and dynamics of foodborne AMR transmission through primary production, water systems, wildlife, and environmental reservoirs.
  • AMR in zoonotic pathogens (e.g., Salmonella, Campylobacter, coli, Staphylococcus aureus) and implications for public health.
  • Impact of antimicrobial use in veterinary practices, animal shelters, agriculture, and aquaculture on resistance emergence and dissemination.
  • Innovative mitigation strategies to combat AMR, including alternatives to antibiotics (e.g., vaccines, bacteriophages, probiotics, antimicrobial peptides).
  • Case and best practices on setting policy, legislation, and stewardship programs (national/regional) for prudent antimicrobial use across sectors.
  • Environmental dimensions of AMR, including contamination of soil, water, and wastewater with antimicrobial residues and resistant bacteria.
  • Socioeconomic and behavioral factors influencing antimicrobial use and resistance spread.
  • Emerging technologies for rapid detection, surveillance, and control of MDR pathogens.

Both original research articles and review papers are welcome. We look forward to receiving your valuable contributions and advancing this exciting and impactful field together.

Dr. Ihab Habib
Guest Editor

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Keywords

  • one health
  • antimicrobial resistance (AMR)
  • multidrug-resistant pathogens
  • zoonoses
  • foodborne AMR
  • environmental reservoirs
  • surveillance and epidemiology

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Published Papers (1 paper)

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Research

13 pages, 1399 KB  
Article
Machine Learning Prediction of Multidrug Resistance in Swine-Derived Campylobacter spp. Using United States Antimicrobial Resistance Surveillance Data (2013–2023)
by Hamid Reza Sodagari, Maryam Ghasemi, Csaba Varga and Ihab Habib
Vet. Sci. 2025, 12(10), 937; https://doi.org/10.3390/vetsci12100937 - 26 Sep 2025
Viewed by 790
Abstract
Campylobacter spp. are leading causes of bacterial gastroenteritis globally. Swine are recognized as an important reservoir for this pathogen. The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) in Campylobacter is a global health concern. Traditional methods for detecting AMR and MDR, [...] Read more.
Campylobacter spp. are leading causes of bacterial gastroenteritis globally. Swine are recognized as an important reservoir for this pathogen. The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) in Campylobacter is a global health concern. Traditional methods for detecting AMR and MDR, such as phenotypic testing or whole-genome sequencing, are resource-intensive and time-consuming. In the present study, we developed and validated a supervised machine learning model to predict MDR status in Campylobacter isolates from swine, using publicly available phenotypic AMR data collected by NARMS from 2013 to 2023. Resistance profiles for seven antimicrobials were used as predictors, and MDR was defined as resistance to at least one agent in three or more antimicrobial classes. The model was trained on 2013–2019 isolates and externally validated using isolates from 2020, 2021, and 2023. Random Forest showed the highest performance (accuracy = 99.87%, Kappa = 0.9962) among five evaluated algorithms, which achieved high balanced accuracy, sensitivity, and specificity in both training and external validation. Our feature importance analysis identified erythromycin, azithromycin, and clindamycin as the most influential predictors of MDR among Campylobacter isolates from swine. Our temporally validated, interpretable model provides a robust, cost-effective tool for predicting MDR in Campylobacter spp. and supports surveillance and early detection in food animal production systems. Full article
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