Wherever multiple organisms coexist, there are population dynamics. This thesis set out to explore the previously less well-described dynamics that arise when a population of pathogenic organisms interact with populations of prey animals and their predators. The main question to be answered was whether prey species can use the pathogens they carry as weapons against their predators, making
susceptibility to diseases not exclusively a problem from an evolutionary standpoint. It turned out that this was indeed the case. We subsequently quantified when this effect would be important.
Finally, we discovered that ecosystems with multiple prey species exhibit chaotic behaviour when affected by an enzootic disease.
The mathematical study of disease dynamics turned out to be highly relevant for the real world after the outbreak of the COVID-19 pandemic. Some of the complexities of disease spread are missed by traditional differential equation models of epidemics. Therefore, we produced several individualbased models for testing and quarantine strategies to mitigate COVID-19. Our models allowed us to investigate the impact of individual differences on epidemics. Under this headline, we examined the interaction between inhomogeneous disease spreading - or superspreading - and population density.
We found that individual differences such as a tendency towards superspreading or inhomogeneous networks may explain some unforeseen dynamics of COVID-19. Finally, we developed evolutionary models that enabled us to guess at how SARS-CoV-2 may evolve in the future. The results of this second section point to a new understanding of epidemic diseases and particularly how to describe them mathematically