@phdthesis{Scherer2019,
  author    = {Scherer, Philipp C{\´e}dric},
  title     = {Infection on the move},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 107, XXXVIII},
  year      = {2019},
  abstract  = {Movement plays a major role in shaping population densities and contact rates among individuals, two factors that are particularly relevant for disease outbreaks. Although any differences in movement behaviour due to individual characteristics of the host and heterogeneity in landscape structure are likely to have considerable consequences for disease dynamics, these mechanisms are neglected in most epidemiological studies. Therefore, developing a general understanding how the interaction of movement behaviour and spatial heterogeneity shapes host densities, contact rates and ultimately pathogen spread is a key question in ecological and epidemiological research. In my thesis, I address this gap using both theoretical and empirical modelling approaches. In the theoretical part of my thesis, I investigated bottom-up effects of individual movement behaviour and landscape structure on host density, contact rates, and ultimately disease dynamics. I extended an established agent-based model that simulates ecological and epidemiological key processes to incorporate explicit movement of host individuals and landscape complexity. Neutral landscape models are a powerful basis for spatially-explicit modelling studies to imitate the complex characteristics of natural landscapes. In chapter 2, the first study of my thesis, I introduce two complementary R packages, NLMR and landscapetools, that I have co-developed to simplify the workflow of simulation and customization of such landscapes. To demonstrate the use of the packages I present a case study using the spatially explicit eco-epidemiological model and show that landscape complexity per se increases the probability of disease persistence. By using simple rules to simulate explicit host movement, I highlight in chapter 3 how disease dynamics are affected by population-level properties emerging from different movement rules leading to differences in the realized movement distance, spatiotemporal host density, and heterogeneity in transmission rates. As a consequence, mechanistic movement decisions based on the underlying landscape or conspecific competition led to considerably higher probabilities than phenomenological random walk approaches due directed movement leading to spatiotemporal differences in host densities. The results of these two chapters highlight the need to explicitly consider spatial heterogeneity and host movement behaviour when theoretical approaches are used to assess control measures to prevent outbreaks or eradicate diseases. In the empirical part of my thesis (chapter 4), I focus on the spatiotemporal dynamics of Classical Swine Fever in a wild boar population by analysing epidemiological data that was collected during an outbreak in Northern Germany persisting for eight years. I show that infection risk exhibits different seasonal patterns on the individual and the regional level. These patterns on the one hand show a higher infection risk in autumn and winter that may arise due to onset of mating behaviour and hunting intensity, which result in increased movement ranges. On the other hand, the increased infection risk of piglets, especially during the birth season, indicates the importance of new susceptible host individuals for local pathogen spread. The findings of this chapter underline the importance of different spatial and temporal scales to understand different components of pathogen spread that can have important implications for disease management. Taken together, the complementary use of theoretical and empirical modelling in my thesis highlights that our inferences about disease dynamics depend heavily on the spatial and temporal resolution used and how the inclusion of explicit mechanisms underlying hosts movement are modelled. My findings are an important step towards the incorporation of spatial heterogeneity and a mechanism-based perspective in eco-epidemiological approaches. This will ultimately lead to an enhanced understanding of the feedbacks of contact rates on pathogen spread and disease persistence that are of paramount importance to improve predictive models at the interface of ecology and epidemiology.},
  language  = {en}
}
@phdthesis{Kuerschner2022,
  author    = {K{\"u}rschner, Tobias},
  title     = {Disease transmission and persistence in dynamic landscapes},
  doi       = {10.25932/publishup-56468},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-564689},
  school      = {Universit{\"a}t Potsdam},
  pages     = {120, LXXIII},
  year      = {2022},
  abstract  = {Infectious diseases are an increasing threat to biodiversity and human health. Therefore, developing a general understanding of the drivers shaping host-pathogen dynamics is of key importance in both ecological and epidemiological research. Disease dynamics are driven by a variety of interacting processes such as individual host behaviour, spatiotemporal resource availability or pathogen traits like virulence and transmission. External drivers such as global change may modify the system conditions and, thus, the disease dynamics. Despite their importance, many of these drivers are often simplified and aggregated in epidemiological models and the interactions among multiple drivers are neglected. In my thesis, I investigate disease dynamics using a mechanistic approach that includes both bottom-up effects - from landscape dynamics to individual movement behaviour - as well as top-down effects - from pathogen virulence on host density and contact rates. To this end, I extended an established spatially explicit individual-based model that simulates epidemiological and ecological processes stochastically, to incorporate a dynamic resource landscape that can be shifted away from the timing of host population-dynamics (chapter 2). I also added the evolution of pathogen virulence along a theoretical virulence-transmission trade-off (chapter 3). In chapter 2, I focus on bottom-up effects, specifically how a temporal shift of resource availability away from the timing of biological events of host-species - as expected under global change - scales up to host-pathogen interactions and disease dynamics. My results show that the formation of temporary disease hotspots in combination with directed individual movement acted as key drivers for pathogen persistence even under highly unfavourable conditions for the host. Even with drivers like global change further increasing the likelihood of unfavourable interactions between host species and their environment, pathogens can continue to persist with heir hosts. In chapter 3, I demonstrate that the top-down effect caused by pathogen-associated mortality on its host population can be mitigated by selection for lower virulent pathogen strains when host densities are reduced through mismatches between seasonal resource availability and host life-history events. I chapter 4, I combined parts of both theoretical models into a new model that includes individual host movement decisions and the evolution of pathogenic virulence to simulate pathogen outbreaks in realistic landscapes. I was able to match simulated patterns of pathogen spread to observed patterns from long-term outbreak data of classical swine fever in wild boar in Northern Germany. The observed disease course was best explained by a simulated high virulent strain, whereas sampling schemes and vaccination campaigns could explain differences in the age-distribution of infected hosts. My model helps to understand and disentangle how the combination of individual decision making and evolution of virulence can act as important drivers of pathogen spread and persistence. As I show across the chapters of this thesis, the interplay of both bottom-up and top-down processes is a key driver of disease dynamics in spatially structured host populations, as they ultimately shape host densities and contact rates among moving individuals. My findings are an important step towards a paradigm shift in disease ecology away from simplified assumptions towards the inclusion of mechanisms, such as complex multi-trophic interactions, and their feedbacks on pathogen spread and disease persistence. The mechanisms presented here should be at the core of realistic predictive and preventive epidemiological models.},
  language  = {en}
}