MAGIcIAN_Modelling approaches to Guide Intelligent surveillance for (DIT.AD021.121)

Thematic area

Engineering, ICT and technologies for energy and transportation

Project area

Structure responsible for the research project

Institute for applied mathematics "Mauro Picone" (IAC)

Project manager

DAVIDE VERGNI
Phone number: 0649937355
Email: d.vergni@iac.cnr.it

Abstract

Antimicrobial resistance (AMR) is increasing worldwide, and surveillance activities play a key role in informing policies to contain AMR. 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. 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.

Goals

Develop approaches and tools in support of (sub-)national prioritisation of AMR surveillance activities, through data science analyses and machine learning techniques;

Develop approaches and tools in support of local targeted and timely AMR surveillance, through holistic dynamic network modelling;

Illustrate in a case study the capacity of these approaches to facilitate the sustainable potential future introduction of novel class and last-resort antimicrobial drugs.

Start date of activity

01/04/2020

Keywords

MAGIcIAN

Last update: 24/04/2024