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The DU Institute of Life Sciences and Technologies is participating in the international project “Modelling Approaches to Guide Intelligent Surveillance for the Sustainable Introduction of Novel Antibiotics” (MAGIcIAN), Contract No. ES RTD/2020/04.
The project is implemented as part of the Horizon 2020 programme ERA-NET JPI-EC-AMR programme “Diagnostics and surveillance of antimicrobial resistance: development of tools, technologies and methods for global use”.
Project implementation period: 01.04.2020 – 31.09.2023
Project partners
National Research Council of Italy
Amsterdam UMC – AMC, Faculty of Medicine, University of Amsterdam
DU Institute of Life Sciences and Technologies,
National Centre for Microbiology, Spain (Instituto de Salud Carlos III)
Center for Disease Dynamics, Economics & Policy, India.
Project summary
The aim of the project is to develop evidence-based guidelines for the introduction of new antibiotics.
There is a growing resistance to antibiotics among disease-causing microorganisms worldwide – microorganisms are evolving in response to antibiotics, resulting in the emergence and survival of new bacterial mutations that are less sensitive to these antibiotics (antimicrobial resistance, AMR). AMR reduces the effectiveness of antibiotics and poses a serious threat to human health. New antibiotics must be introduced in a considered manner to help patients who need them most and to slow down the development of antimicrobial resistance worldwide. The aim of the project is to bring together experts in medicine, microbiology, epidemiology and mathematical modelling in a single project and to analyse the available data.
The project involves AMR modeling and prediction at three levels: processes in the patient (micro level), in the community with which the patient comes into contact (meso level), and at the national or large regional level (macro level).
The DU project group is working on AMR macro-level modeling in the case of community-acquired pathogens. In order to develop guidelines for the introduction of new antibiotics, it is necessary to know the number or proportion of resistant bacteria carriers (infected people) in a country or region. The number of resistant infections is recorded in only a small proportion of countries worldwide, so it is necessary to predict AMR levels in countries and regions where no measurements have been made. Such forecasts will save time and other resources. The project partners have developed methods for forecasting AMR levels using mathematics and machine learning methods, see (Oldenkamp et al., 2021), (Daugulis et al., 2022). It is assumed that the spread of community-acquired diseases depends on socio-economic factors. It is assumed that the spread of diseases acquired in the community depends on socio-economic factors. Using such data as initial data, AMR level forecasts are made. Data from the World Bank is used.
Once AMR forecasts have been obtained, statistical analysis, geographical correlation and other types of analysis can be performed to identify countries and territories where the introduction of new antibiotics for a given pathogen is desirable or where additional measures are needed.
Project website: https://www.magician-amr.eu/
Project coordinator: Pēteris Daugulis
Project implementers: DU employees and external experts.
2020.gads
Aprīlis: DU noslēdz līgumu ar VIAA par projekta pirmā posma īstenošanu.
2021.gads
Jūnijs: DU noslēdz līgumu ar VIAA par projekta otrā posma īstenošanu.
https://www.pnas.org/doi/10.1073/pnas.2013515118
2022.gads
Janvāris: PROJEKTA “MAGICIAN” IETVAROS PUBLICĒTS RAKSTS “A PCA-based data prediction method”
Jūnijs: DU noslēdz līgumu ar LZP par projekta trešā posma īstenošanu.
Tika iegūtas ikgadējas AMR prognozes slimības izraisītāja Neisseria gonorrhoeae gadījumā lielākajai daļai pasaules valstu laika periodam 1997-2021 gg. Tika veikta šo prognožu analīze, kas ir pieejama projekta partneriem, kas nodarbojas ar vadlīniju un rekomendāciju izstrādi.
Bibliogrāfija
Oldenkamp R., Schultsz C., Mancini E., Cappuccio A. (2021) Filling the gaps in the global prevalence map of clinical antimicrobial resistance, Proc Natl Acad Sci U S A, 2021 Jan 5;118(1):e2013515118. doi: 10.1073/pnas.2013515118. Erratum in: Proc Natl Acad Sci U S A. 2021 Oct 19;118(42): PMID: 33372157; PMCID: PMC7817194.
Daugulis P., Vagale V., Mancini E., Castiglione F. (2022) A PCA-based data prediction method, Baltic J. Modern Computing, Vol 10-1, pp.1-16.
