Bayesian inference and model selection for stochastic epidemics

Project description

Novel epidemiology statistics: lessons from COVID-19

The SARS-Cov-2 pandemic has highlighted the importance of prompt identification of virus outbreaks and optimal control strategies. The EU-funded BERNADETTE project is working on novel statistical methodology for the modelling of infectious diseases like COVID-19. This work is expected to help scientists understand various disease aspects and quantify the effect of non-pharmaceutical interventions. Moreover, it will address the socio-economic impact of different response strategies for human epidemics and support decision-making. The implementation of this model will improve public health policies and preparedness planning in Europe and the rest of the world.

Fields of science

• social sciences › economics and business › economics
• medical and health sciences › health sciences › public health › epidemiology
• natural sciences › mathematics › applied mathematics › statistics and probability › bayesian statistics
• medical and health sciences › health sciences › infectious diseases › RNA viruses › coronaviruses

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility

Topic(s)

• MSCA-IF-2020 - Individual Fellowships

Call for proposal

H2020-MSCA-IF-2020

See other projects for this call

Funding Scheme

MSCA-IF-EF-CAR - CAR – Career Restart panel

Coordinator

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER

Net EU contribution

€ 165 085,44

Address

Kefallinias Street 46
11251 Athens
Greece

Region

Κεντρικός Τομέας Αθηνών › Aττική › Αττική

Activity type

Higher or Secondary Education Establishments

Links

Non-EU contribution

€ 0,00