Subproject 1: INSIDe-Plattform

In SP1, we are developing the INSIDe software platform to integrate several mathematical models that model different aspects of infection events based on different data into one complex model.
In recent years, various models have already been developed for simulating the spread of infectious diseases. However, these are limited in their prediction accuracy. This is among other things due to insufficient data basis, as the models do not allow for the integration of wastewater observations, for example. By combining different models based on different data using the INSIDe platform, we aim to improve the predictive accuracy of the simulation. This will result in a simpler assessment and prediction of the spread of infectious diseases.
Specifically, we will integrate the epidemiological models developed in SP2 for modelling disease transmission with the sewer network models developed in SP 3 and 4 for modelling the spread of virus particles in wastewater. We will integrate the platform with extensive datasets from studies of the spread of SARS-CoV-2 in Munich, Germany, and Addis Ababa, Ethiopia (see also SP5). The differences between these two cities in terms of population structure, access to healthcare and vaccination, government policies to contain SARS-CoV-2 and climate will allow us to comprehensively evaluate the platform and its flexibility.

The INSIDe platform will be modular so that models can be exchanged or added, e.g. a module on the impact of disease mitigation measures (see SP2). The modular design allows for flexible adaptation of the platform for different diseases and scenarios, e.g. transfer to other cities or regions.

In addition to simulation, the INSIDe platform will enable the derivation of unknown model parameters from experimental data, e.g. on the effect of infection control measures, and support experimental designs, e.g. by identifying the best locations for additional wastewater sampling to improve the data source for parameter estimations and predictions.

The platform will be developed with open source software tools and standardised interfaces to enable interoperability and use by other researchers.

Contact: Dr. Christina Fricke, inside@uni-bonn.de