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

Area tematica

Ingegneria, ICT e tecnologie per l'energia e i trasporti

Area progettuale

Struttura responsabile del progetto di ricerca

Istituto per le applicazioni del calcolo "Mauro Picone" (IAC)

Responsabile di progetto

DAVIDE VERGNI
Telefono: 0649937355
E-mail: 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.

Obiettivi

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.

Data inizio attività

01/04/2020

Parole chiave

MAGIcIAN

Ultimo aggiornamento: 27/03/2023