BERNADETTE · Bayesian inference and model selection for stochastic epidemics

Bayesian inference and model selection for stochastic epidemics

The Marie Sklodowska-Curie Action (MSCA) titled “Bayesian inference and model selection for stochastic epidemics” (BERNADETTE) addressed the issue of developing novel statistical methodology for the modeling of transmission dynamics of infectious diseases like COVID-19. This work is driven by the challenges of under-ascertainment of COVID-19 infections and the presence of heterogeneity in type, relevance, and granularity of the data. No single dataset can provide enough information on its own to estimate disease transmission, but estimation is feasible by synthesizing multiple datasets. Bayesian evidence synthesis combines expert knowledge and multiple data sources like streams of surveillance data and ad-hoc studies in a coherent manner, linking different data sources through equations that describe how data sources, quantities under estimation, and model parameters relate to each other. The project focused on proposing Bayesian evidence synthesis approaches for the analysis of COVID-19 data, with the aim to: (i) infer the true number of infections using daily COVID-19 attributable mortality counts; (ii) learn the age-specific transmission rates; (iii) reconstruct the epidemic drivers from publicly available data sources. The objectives of the BERNADETTE MSCA have been (i) the study of Bayesian evidence synthesis methods, identification of COVID-19 data sources, specification of an epidemic model for expressing transmission dynamics and selection of appropriate prior distributions for the time-varying model parameters; (ii) combination of the aforementioned components through a hierarchical model to develop a holistic framework for quantifying the effect of intervention measures and estimating key epidemiological parameters and implementation of the developed methodology to COVID-19 data from European countries; (iii) development of a statistical tool for improving computational efficiency and prediction accuracy; (iv) reproducibility of research. In parallel, the action aimed at ensuring further development of additional competencies that will be fundamental for the Fellow reaching a position of professional maturity. The originality and innovative nature of BERNADETTE will contribute to healthcare research. Despite the substantial advancements in Bayesian evidence synthesis in the last few years, there is a need for model building strategies of increasing realism and complexity to better inform infection control policies. BERNADETTE proposes novel approaches for providing evidence-based knowledge of COVID-19 transmission while facilitating model realism and explainability, and for supporting healthcare services. The BERNADETTE outputs will assist in improving European preparedness planning and support decision-making in the framework of national epidemic preparedness plans.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз