Despite huge efforts, infectious diseases still form a major threat for humanity. The interaction of empirical and mathematical sciences contributed much to our knowledge and to the control of infectious diseases. Perhaps the largest challenge today is the use of pathogen sequence data to understand and predict the spread of epidemics, improve disease control measures, and optimize drug applications and vaccination strategies. For example, genetic pathogen data and phylogenetic trees proved their usefulness in locating the origin of the Ebola outbreak from 2015.
However, current research largely neglects a common trait of pathogens, namely their ability to have quiescence or dormancy states. Resting stages appear at two levels: 1) parasite spores can become dormant in an infected host e.g. with the effect that drugs fail to fight the infection, or 2) parasite can show a latency period upon infection and hosts carry the infection for an unexpected long time period (asymptomatic patients).
We study the resting stages’ effects on 1) the infection dynamics in a population, 2) the contact structure and disease transmission routes, and 3) the genetic diversity of parasites. We investigate mathematical models of infection dynamics at the within- and between-host levels to develop inference methods from full genome data of parasites, to estimate the parameters of the epidemics, and to infer infection routes. Our results will shed new light on the genomic evolution during epidemics of human parasites such as tuberculosis and malaria and of recent devastating forest trees pathogens.