Understanding superspreading phenomena of epidemics on complex networks

The covid19 pandemic shows that existing models of epidemiology do not explain well the phenomena of superspreaders and superspreading events.

In this research we aim to develop new probabilistic models that allow for and explain such phenomena. The methods will be based on methods from spatial probability [such as random graph models] and extreme value theory [in particular heavy tails]. We intend to also explore algorithmic challenges, such as designing effective contact tracing schemes.