Modelling Approaches to Guide Intelligent surveillance for the sustainable Introduction of novel ANtibiotics
Antimicrobial resistance (AMR) is increasing worldwide, and surveillance activities play a key role in informing policies to contain AMR. Moreover, resistance to new antibiotics is emerging ever quicker after their introduction onto the market, rapidly reducing the effectiveness of even last-resort antibiotics. As such, the sustainable introduction of a novel class antibiotic can only be achieved when accompanied by timely and informed surveillance and stewardship strategies. Affordable methodologies and tools to estimate the extent of national and local AMR are urgently needed to intelligently prioritise surveillance efforts, especially in low- and middle-income countries (LMICs). Combining clinical, microbiological, epidemiological, and computational modelling expertise in one consortium, the project aims to satisfy that need through advanced data science and machine learning techniques at the global and (sub-)national scale, and multi-scale holistic dynamic network models at the local scale. Data and models resulting from the project will be disseminated to the benefit of various stakeholders, via their active participation in the project’s sounding board workshop, their involvement in our advisory board, the release of data and models in an international repository, and the initiation of integrating our modelling system in a web-based interactive platform. Ultimately, our approaches aim to facilitate the sustainable potential future introduction of novel class and last-resort antimicrobial drugs. We illustrate this capacity in the specific case of Neisseria gonorrhoeae.
- Davide Vergni, National Research Council of Italy (CNR) - Institute for Applied Computing (IAC) "M. Picone", Italy (Coordinator)
- Constance Schultsz, Amsterdam Institute for Global Health and Development, Amsterdam UMC, Netherlands (Partner)
- Peteris Daugulis, Daugavpils University, Institute of Life Sciences and Technology, Mathematical Research Center, Latvia (Partner)
- Raquel Abad Torreblanca, Instituto de Salud Carlos III, Spain (Partner)
Pseudomonas aeruginosa causes severe infections in hospitalized patients. Carbapenem antibiotics are among the most effective antibiotics for treating P. aeruginosa infections. The worldwide emergence of carbapenem-resistant P. aeruginosa (CR-PA) makes infections by these pathogens almost untreatable. The World Health Organization now ranks CR-PA highest in the list of ‘urgent threats’. Information for action to prevent further emergence has to come from insight into sources and transmission routes through smart surveillance. At present, a smart surveillance strategy is not available for CR-PA. The aim of this project is to develop a globally-applicable smart surveillance strategy to guide action against the spread of CR-PA. Since P. aeruginosa prefers moist niches, we will focus on the human-water interface. First, highly-sensitive methods to detect CR-PA in specific environmental and human niches will be developed. Subsequently, CRPA will be collected in three study sites with increasing prevalences of CR-PA, increasingly warmer climates, and different water situations: Rotterdam (The Netherlands), Rome (Italy), Jakarta (Indonesia). CR-PA will be searched for in a variety of niches in the environment outside and inside the hospital, and in healthy humans and hospitalized patients. Whole genome sequencing will be performed to compare the CR-PA from different sources and identify transmission routes. Our project will provide insight into the relative contribution of the different potential reservoirs of CR-PA to its spread in different settings which will be used for the development of a globally-applicable surveillance strategy for CR-PA to guide preventive actions.
- PNAS, 2020. Filling the gaps in the global prevalence map of clinical antimicrobial resistance
- Sci Rep, 2021. In silico designing of vaccine candidate against Clostridium difficile.
- IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022. An agent-based multi-level model to study the spread of antimicrobial-resistant gonorrhoea
- Frontiers in Applied Mathematics and Statistics, 2023. An agent-based multi-level model to study the spread of gonorrhea in different and interacting risk groups