Modelling the transmission and burden of ESBL-producing Enterobacterales from a One Health perspective in Singapore
6 affiliations
- 1Programme in Health Services & Population Health Research, Duke-NUS Medical School, Singapore; SingHealth Duke-NUS Global Health Institute, Duke-NUS Medical School, Singapore
- 2Programme in Health Services & Population Health Research, Duke-NUS Medical School, Singapore
- 3Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- 4Programme in Health Services & Population Health Research, Duke-NUS Medical School, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- 5Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Health Intervention and Technology Assessment Program, Ministry of Public Health, Nonthaburi, Thailand
- 6Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Infectious Diseases Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Corresponding author at: Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Abstract
The long-term burden of antimicrobial resistance (AMR) in hyper-urbanised settings, shaped by complex, multi-sectoral transmission dynamics across human, animal, and environmental reservoirs, remains poorly understood. Using Singapore as a case study, we applied a dynamic One Health modelling framework to explore how ESBL-producing Enterobacterales infections and deaths may evolve over the next decade and identify critical control pathways. We developed a deterministic, compartmental One Health transmission model, the first to integrate hospital, community, companion animal, and environmental reservoirs, calibrated to ten years of national hospital surveillance data (2012−2021) using Markov Chain Monte Carlo Bayesian methods. We projected the baseline disease burden over a 10-year reporting horizon and compared it with counterfactual scenarios while employing global and local sensitivity analyses to identify influential parameters. Over this period, the model projected a total burden of 208,179 clinical ESBL-producing Enterobacterales infections (95% uncertainty interval [UI] 121,669–307,053) and 5567 (95% UI 3660–7806) deaths, with 3491 deaths specifically attributable to ESBL-producing Enterobacterales. Community decolonization strongly influenced infection burdens in both community and hospital settings; a 100% reduction in this process was projected to add nearly 1.8 million additional infections and more than 45,000 resistant-infection deaths across hospital and community settings in Singapore over 10 years. Our dynamic One Health model quantifies these inter-sectoral transmission pathways and identifies clear intervention priorities. While preventing progression from colonisation to infection is vital for reducing hospital-specific mortality, comprehensive system-wide control may benefit from targeting the community reservoir. Ultimately, the modelling framework developed here provides a robust, transferable tool for other global cities to design and evaluate integrated One Health AMR control strategies to mitigate urgent public health threats and associated socio-economic consequences for urban populations and global healthcare systems.
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BibTeX
@article{xie2026modelling,
title = {{Modelling the transmission and burden of ESBL-producing Enterobacterales from a One Health perspective in Singapore}},
author = {Yewei Xie and Nicholas Graves and Zhi Zhen Lim and Alec Morton and Isabelle Maya Srivastava and Yot Teerawattananon and Hwee Lin Wee and Yin Mo},
journal = {One Health},
year = {2026},
month = oct,
doi = {10.1016/j.onehlt.2026.101631},
url = {https://doi.org/10.1016/j.onehlt.2026.101631},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Modelling the transmission and burden of ESBL-producing Enterobacterales from a One Health perspective in Singapore
AU - Yewei Xie
AU - Nicholas Graves
AU - Zhi Zhen Lim
AU - Alec Morton
AU - Isabelle Maya Srivastava
AU - Yot Teerawattananon
AU - Hwee Lin Wee
AU - Yin Mo
PY - 2026
DA - 2026/10/23
JO - One Health
DO - 10.1016/j.onehlt.2026.101631
UR - https://doi.org/10.1016/j.onehlt.2026.101631
AB - The long-term burden of antimicrobial resistance (AMR) in hyper-urbanised settings, shaped by complex, multi-sectoral transmission dynamics across human, animal, and environmental reservoirs, remains poorly understood. Using Singapore as a case study, we applied a dynamic One Health modelling framework to explore how ESBL-producing Enterobacterales infections and deaths may evolve over the next decade and identify critical control pathways. We developed a deterministic, compartmental One Health transmission model, the first to integrate hospital, community, companion animal, and environmental reservoirs, calibrated to ten years of national hospital surveillance data (2012−2021) using Markov Chain Monte Carlo Bayesian methods. We projected the baseline disease burden over a 10-year reporting horizon and compared it with counterfactual scenarios while employing global and local sensitivity analyses to identify influential parameters. Over this period, the model projected a total burden of 208,179 clinical ESBL-producing Enterobacterales infections (95% uncertainty interval [UI] 121,669–307,053) and 5567 (95% UI 3660–7806) deaths, with 3491 deaths specifically attributable to ESBL-producing Enterobacterales. Community decolonization strongly influenced infection burdens in both community and hospital settings; a 100% reduction in this process was projected to add nearly 1.8 million additional infections and more than 45,000 resistant-infection deaths across hospital and community settings in Singapore over 10 years. Our dynamic One Health model quantifies these inter-sectoral transmission pathways and identifies clear intervention priorities. While preventing progression from colonisation to infection is vital for reducing hospital-specific mortality, comprehensive system-wide control may benefit from targeting the community reservoir. Ultimately, the modelling framework developed here provides a robust, transferable tool for other global cities to design and evaluate integrated One Health AMR control strategies to mitigate urgent public health threats and associated socio-economic consequences for urban populations and global healthcare systems.
SN - 2352-7714
ER -