Data analysis and mechanistic-based models and projections
The project for data integration by analysis and modelling will build on the achievements and gathered expertise during the monitoring phase of the epidemics.
What are the project objectives?
The overarching strategy for data integration will be based on data analysis and subsequent integration into mechanistic epidemiological models. For this purpose, data gathered in project dedicated to data management, interoperability and integrated analysis will be analysed in a time-specific manner to reflect the distinct phases of the pandemics.
For subsequent data mining and mechanistic interpretation, the data will be integrated in Luxembourg-specific mechanistic models as done e.g. for the impact analysis of mass screening or investigating vaccination strategies. In the context of CoVaLux, we will target the models towards specific research questions. Since the agent-based model used for the mass screening analysis is already based on the socio-economic structure of the Luxembourg society, it can directly be adapted to investigate long COVID effects in specific subpopulations.
The integration of long COVID symptoms and vaccination effects will be also done by a more homogenous, classical epidemiological modelling approach (extended SEIR models), which allow for data-driven and machine-learning based parameterisation as e.g. recently done by an Extended Kalman Filter to determine the shedding population from virus prevalence in wastewater data. In addition, we will also apply machine-learning approaches to the labelled data from the different cohorts to identify biomarkers for long COVID symptoms in a vaccination-specific manner.
By this data integration strategy, the project will not only support the development of mechanism-based understanding of long COVID but will also provide predictive frameworks for future projections of pandemics based effects. In close collaboration with the long COVID Consultation network, a first-level patient engagement will be established by providing a patient-app for the healthcare service such as remote monitoring and disease management.
The app will support the telemonitoring service for long COVID patient management, enable patient engagement (feedback from the findings of the CoVaLux programme), and link patients to the long COVID long-term data-assessment. It will also serve future digital patient management strategies in Luxembourg.
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