Pandemic Preparedness: One Health, One Future

The Project Synthetic Health Data for Research and Policy which is part of the Leibniz Lab "Pandemic Preparedness" aims to develop methods for generating synthetic health data for pandemic research and policy advice. The project will involve several key steps:

1. Data Review: Analysis and categorization of existing health datasets in Germany, focusing on infectious diseases and personal or contextual factors.
2. Method Review: Examination of data matching techniques and statistical learning methods (e.g., mice) to create synthetic databases.
3. Method Development: Design of a new statistical method for generating synthetic individual-level health data.
4. Implementation: Coding this method in R and preparing documentation.
5. Validation: Testing the developed methods with cooperating institutes.
6. Analysis: Conducting statistical evaluations on the influence of factors such as social contacts and age on infection probabilities.
7. Prediction Models: Collaborating with health experts to develop predictive models for infection risk.
8. Dashboard: Creation of a risk assessment dashboard for infection dynamics in different subpopulations.

In year one, the initial steps (i-iii) and a prototype for step (iv) will be completed. Year two focuses on finalizing step (iv) and progressing to validation and model development. Year three will optimize resource allocation based on forecast scenarios, aiding pandemic management decisions, such as vaccine distribution.

The project is coordinated by SOEP leveraging its access to critical datasets and expertise in empirical and statistical methods. The end result will provide unique, evidence-based tools to support policymakers during pandemics.