Bayesian inference and model selection for stochastic epidemics

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.