BERNADETTE | Bayesian inference and model selection for stochastic epidemics

Summary
The ultimate goal of this Fellowship, titled “Bayesian infEReNce And moDel sElecTion for sTochastic Epidemics” (BERNADETTE), is to train a talented researcher through a research project focused on the development of novel statistical methodology for the modeling of infectious diseases like COVID-19. The success of the interdisciplinary project will lead to a number of multidisciplinary innovations in epidemiology, Public Health policy and statistics, which will contribute to the timely identification of optimal disease control strategies. The Fellow – Dr. Lampros Bouranis – will be trained in the fields of statistics and epidemiology, receiving access to a unique training experience at the host – Department of Statistics, Athens University of Economics and Business (AUEB) – and co-hosts. The BERNADETTE outputs will be relevant to healthcare and the EU Epidemic intelligence, by: i) offering novel statistical methodology for the analysis of COVID-19 outbreak data and the description of a number of aspects of the underlying infection pathway of the disease, ii) quantifying the effect of non-pharmaceutical interventions based on an epidemic model, iii) allowing for the forecasting of future case number scenarios, iv) contributing in the assessment of the socio-economic impact of different response strategies for human epidemics in Europe in order to improve European preparedness planning and support decision-making in the framework of national epidemic preparedness plans. The BERNADETTE outputs will contribute to the enhancement of EU scientific excellence. Additionally, the project will enable the establishment of a long-term collaboration between the host and co-hosts, bringing the centers of European research excellence together.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101027218
Start date: 10-05-2021
End date: 24-06-2023
Total budget - Public funding: 165 085,44 Euro - 165 085,00 Euro
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Original description

The ultimate goal of this Fellowship, titled “Bayesian infEReNce And moDel sElecTion for sTochastic Epidemics” (BERNADETTE), is to train a talented researcher through a research project focused on the development of novel statistical methodology for the modeling of infectious diseases like COVID-19. The success of the interdisciplinary project will lead to a number of multidisciplinary innovations in epidemiology, Public Health policy and statistics, which will contribute to the timely identification of optimal disease control strategies. The Fellow – Dr. Lampros Bouranis – will be trained in the fields of statistics and epidemiology, receiving access to a unique training experience at the host – Department of Statistics, Athens University of Economics and Business (AUEB) – and co-hosts. The BERNADETTE outputs will be relevant to healthcare and the EU Epidemic intelligence, by: i) offering novel statistical methodology for the analysis of COVID-19 outbreak data and the description of a number of aspects of the underlying infection pathway of the disease, ii) quantifying the effect of non-pharmaceutical interventions based on an epidemic model, iii) allowing for the forecasting of future case number scenarios, iv) contributing in the assessment of the socio-economic impact of different response strategies for human epidemics in Europe in order to improve European preparedness planning and support decision-making in the framework of national epidemic preparedness plans. The BERNADETTE outputs will contribute to the enhancement of EU scientific excellence. Additionally, the project will enable the establishment of a long-term collaboration between the host and co-hosts, bringing the centers of European research excellence together.

Status

CLOSED

Call topic

MSCA-IF-2020

Update Date

28-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.3. EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions (MSCA)
H2020-EU.1.3.2. Nurturing excellence by means of cross-border and cross-sector mobility
H2020-MSCA-IF-2020
MSCA-IF-2020 Individual Fellowships