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Network Based Coarse Graining of an Infection Transmission Model

In response to the outbreak of SARS-Cov2 Pandemic, the Klipp Lab’s developed an individualized GEoReferenced Demographic Agent-based model (GERDA)  to allow for detailed infection transmission and desease progression simulation of real world communities.

The open source model bases exlusively on publicly available data including geolocation provided by openstreetmap.org and deases progression information from the Robert Koch-Institut. In the model indivuals are modeled as agent with specific characterisitcs, such as age or occupation, following typical daily routines according to those characteristics. Infection transmission and deseas progression are implemented as stochasitical time dependent events. To optimize, upscale and further analyze the model we are investigating the resulting dynamic interaction networks.

Your responsibility will be, depending on both your personal interests and the task at hand, to generate and compare interaction networks between different communities and to employ graph theory to reduce those networks while maintaing their characteristics.

 

 

Topics and Keywords

Agent Based Models, Graph Theory, Infection Transmission

 

Tasks and Milestones

1.  Understanding of the model

2.  Extracting and comparison of dynamical networks from GERDA for different communities

3.  Find an formulate a coarse grain approach, based on those results

5.  Report writing



Literature

Goldenbogen, B., Adler, S. O., Bodeit, O., Wodke, J. A. H., Korman, A., Krantz, M., … Klipp, E. (2020). Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks. MedRxiv.

Goldenbogen, B., Adler, S. O., Bodeit, O., Wodke, J. A. H., Korman, A., Bonn, L., … Klipp, E. (2020). Geospatial precision simulations of community confined human interactions during SARS-CoV-2 transmission reveals bimodal intervention outcomes. MedRxiv.