Subproject 1 is concerned with the collection and processing of data from various sources. These are relevant for use in the subsequent subprojects and form the basis for the development of LOKI and the underlying models. The goal is to integrate multiple data sources into one data pool for models and forecasts in a structured and easily usable way. This includes data collection, structuring, and primary analysis. In addition, the potential for integrating new data sources is analyzed. These data are either publicly available or are generated through collaborations with the pilot health departments. Furthermore, Subproject 1 addresses privacy issues. A major limitation in assessing and predicting epidemic dynamics is the availability, quality, and origin of data. Integrating a variety of independent data sources, standardizing them, and automating their processing are essential for downstream statistical analysis and accurate predictions. Quality control mechanisms are established to ensure high accuracy of data and metadata with minimal manual maintenance. At the same time, data is aggregated, combined, and converted into standardized formats to ensure a reliable and predictable data source for automated workflows.

Epidemiological models must be calibrated for predictions. However, reliable estimation of model parameters from insufficient data is often impossible. This problem is exacerbated as the level of model detail increases. Directly measurable model parameters derived from data provide valuable information, and their use improves the reliability of predictions.

Primary data sources include aggregated public surveillance data, case-based data from local health authorities, data on clinical and hospital parameters (in collaboration with the Lean European Open Survey on SARS-CoV-2 [LEOSS] project, a prospective European multicenter cohort study), population-based studies, and seroprevalence surveys (https://serohub.net/en/, https://hzi-c19-antikoerperstudie.de/).

In addition, Subproject 1 integrates existing data on vaccination, wastewater sampling, contact networks, mobility, and aerosol dynamics.

In addition, an evidence synthesis of existing literature will be conducted to integrate previous estimates of parameters. The evidence synthesis will focus on SARS-CoV-2 in the first phase and later integrate ongoing work (https://respinow.de/) on other respiratory pathogens.

These data and the synthesized evidence on model parameters will be made available in a structured form for downstream subprojects to use directly in pattern recognition approaches, mechanical models, and in model calibration and uncertainty analysis. To promote synergies with existing international projects, Subproject 1 will seek to incorporate and compare the current platform in an overarching review of similar global projects to build modeling and prediction platforms for use by public health agencies.

Several areas of the subproject deal with sensitive data. The data collected will be analyzed for its potential to violate privacy and the risk from inferences will be examined. Similarly, it will be ensured that the products of data analysis or algorithms do not expose private data and are not vulnerable to inference attacks. Where necessary, local mechanisms will be deployed and developed. Data will be aggregated and analysis coarsened to ensure individual privacy is protected.

Aggregation of a variety of data sources will provide a detailed picture of the pandemic and its parameters, allowing unprecedented precision of prediction in a thoughtful, semi-automated manner accessible to public health workers and other relevant professionals.

Objective

The goal is to integrate multiple data sources into one data pool for models and forecasts in a structured and easily usable form (for use in the following subprojects).