Opendata, web and dolomites

EstAMR SIGNED

Estimating the Prevalence of AntiMicrobial Resistance

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "EstAMR" data sheet

The following table provides information about the project.

Coordinator
SCHWEIZERISCHES TROPEN- UND PUBLIC HEALTH-INSTITUT 

Organization address
address: SOCINSTRASSE 57
city: Basel
postcode: CH-4002
website: www.swisstph.ch

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 203˙149 €
 EC max contribution 203˙149 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2020
 Duration (year-month-day) from 2020-04-01   to  2022-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    SCHWEIZERISCHES TROPEN- UND PUBLIC HEALTH-INSTITUT CH (Basel) coordinator 203˙149.00

Map

 Project objective

Antimicrobial resistance (AMR) is the ability of an infection to stop antimicrobials, such as antibiotics, antivirals and antimalarials, from working against it. In the EU, every year AMR is responsible for thousands of deaths and costs millions of euros. Yet forecasting the prevalence of AMR remains an open challenge. This project meets this challenge by developing cutting-edge Hierarchical Bayesian models (HBMs). As a case study, the models will focus on the malaria parasite Plasmodium falciparum that has developed resistance to sulfadoxine/pyrimethamine between 1994 and 2016. This ensures that the goals are realistic, with immediate insight and impact, whilst also remaining methodologically relevant for all AMR. Within the field of ecology, regression models are being replaced so as to incorporate more complex non-linear relationships between abundance and the environment. Unlike traditional methods, HBMs are spatiotemporal models that (i) account for varying geography (ii) separate underlying processes and (iii) include uncertainty in the data and model parameters. This thorough package has proven to yield more insight and accuracy. By using partial differential equations, HBMs separate occurrence due to spread and occurrence due to emergence. Despite its relevance, differentiating between these two process is currently unexplored in epidemiology. Furthermore, when modelling AMR, infections competing for hosts is a process which is currently unexplored in HBMs. Thus, the two long term contributions of this project are: Bringing HBMs to epidemiology to gain a better understanding of underlying processes, and advancing the field of HBMs to include more complex dynamics. And more immediately, the two key contributions are: Quantifying the dynamics and influencers of the spread of drug resistant malaria, and forecasting the frequency of partially drug resistant malaria, and fully drug resistant malaria, at different locations and at different times.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "ESTAMR" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "ESTAMR" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.3.2.)

ACES (2019)

Antarctic Cyclones: Expression in Sea Ice

Read More  

TOPOCIRCUS (2019)

Simulations of Topological Phases in Superconducting Circuits

Read More  

GENESE 17 (2018)

Geometries of Exotic NuclEar StructurE 17

Read More