Infection dynamics at within-host and between-host scales

Developing and predicting the effect of control measures on the infection dynamics in parasite-host systems with many feedback loops between the different infection processes poses a challenge. Part of this challenge comes from the large heterogeneity often observed in these systems. The goal of this PhD thesis is to increase our understanding of these complex within and between-host infection dynamics through the creation of mathematical and computational models that are able to capture the existing host and/or parasite heterogeneity. This goal is reached through a series of research projects that gradually build up in complexity of both the system modelled and the modelling techniques used. In the first project a mathematical model is built to help understand a large body of experimental results on Experimental Autoimmune Encephalomyelitis (EAE), a mouse-model for the human autoimmune disease Multiple Sclerosis. The model is used to reduce the complexity of the experimental results providing new insights and allowing more focussed continued. The second project looks at Paratuberculosis in cows, a chronic infection without cure caused by Mycobacterium avium subspecies paratuberculosis (MAP). A mathematical model of MAP infection is built to study the difference of Type 1 and Type 2 immune responses on disease progression. The results, amongst others, show that a Type 1 response that can stop the initial growth of infection does not necessarily have the critical killing rate needed to control the infection. In the third project a computational model is built of Coccidiosis in broiler chickens caused by the protozoan parasite Eimeria acervulina. The model is built to understand the progression of infection in flocks of commercial broiler chickens. The model confirms previous findings of a wave-like relationship between the cleaning intensity in between subsequent flocks and the severity of outbreaks and gives an explanation for the cause of this wave-like relationship. The fourth and fifth projects study malaria in humans caused by the Plasmodium falciparum parasite. This parasite uses a group of variant surface antigens (VSA) to escape host immunity which play an important role in disease severity due to the ability they give infected red blood cells to adhere to different host tissues. In the fourth project a computational model is built to better understand the role of these VSA on host immunity and parasite evolution. Amongst other results, the model shows how hosts do not acquire antibodies against VSA at random, but instead in a general order where immunity against stronger adhering VSA is acquired earlier. The fifth project studies the effect of current and potential control measures on the sets of VSA carried by the circulating parasites. The results, amongst others, show that some seemingly sensible measures can have the unwanted effect of increasing the frequency of parasites carrying stronger adhering VSA, such as those causing cerebral malaria. The models created in these five projects help to explain a wide range of experimental results, are used to predict the effect of control measures and generate ideas for the development of new methods of control.