Effect of density dependence on coinfection dynamics

In this paper we develop an SIR model for coinfection. We discuss how the underlying dynamics depends on the carrying capacity $K$: from a simple dynamics to a more complicated. This can help in understanding of appearance of more complicated dynamics, for example, chaos etc. The density dependent population growth is also considered. It is presented that pathogens can invade in population and their invasion depends on the carrying capacity $K$ which shows that the progression of disease in population depends on carrying capacity. Our approach is based on a bifurcation analysis which allows to generalize considerably the previous Lotka-Volterra type models.Comment: 23 page

Virulence Evolution of Fungal Pathogens in Social and Solitary Bees with an Emphasis on Multiple Infections

The health of pollinators, especially bees, is of the utmost importance to success of many agricultural ecosystems. Microorganisms can cause diseases in bees; such microbes are pathogenic. The ability of a pathogen to cause harm to its host (such as a bee) is termed its virulence. Studying the evolution of different levels of virulence can lead researchers to a better understanding of pathogens, and potentially predict how much harm a pathogen can cause in the future. We studied the evolution of virulence levels for a fungal disease of bees. This group of fungi is composed of 28 species, and some cause a disease in bees called chalkbrood while others do not. Using what we know about virulence evolution we wanted to see if the pathogens could infect all bees, if the pathogens varied in virulence when infecting at the same time as another pathogen, and if solitary bees had any behavioral adaptations that might increase or decrease chalkbrood infection. By using DNA sequences, the relationship between the genetic structures of each of the fungal species was studied, and we found that pathogens of solitary bees grouped together while pathogens of social bees (honey bees) were not part of this group. We then found that a solitary bee pathogen did not infect honey bees very well, and vice versa. The nuances of the relationship between two solitary bee pathogens were examined more closely to determine how the two pathogens interact in this bee. In this case, under varying conditions of infection, one pathogen always maintained a similar level of virulence and spore production, while the other pathogen varied in these measures. In addition, when doses of these fungi were fed to bee larvae at different times, more bees survived than when the doses were given at the same time, suggesting that bee immune responses are very important. Finally, we found no evidence of any specific behaviors of solitary bees exposed to infective spores that would suggest these bees have behaviors that are evolved to alter chalkbrood levels in populations

Dynamics of simultaneous epidemics on complex graphs

The subject of this thesis is the study of a system of multiple simultaneously spreading diseases, or strains of diseases, in a structured host population. The disease spread is modelled using the well-studied SEIR compartmental model; host population structure is imposed through the use of random graphs, in which each host individual is explicitly connected to a predetermined set of other individuals. Two different graph structures are used: Zipf power-law distributed graphs, in which individuals vary greatly in their number of contacts; and Poisson distributed graphs, in which there is very little variation in the number of contacts. Three separate explorations are undertaken. In the first, the extent to which two SEIR processes will overlap due to chance is examined in the case where they do not affect each other's ability to spread. The overlap is found to increase with increased heterogeneity in the number of contacts, all things equal. Introducing differences in infection probability or a delay between introducing the two strains produces more complex dynamics. I then extend the model to allow strains to modify each other's transmissibility. This is found to lead to modest changes in the size of the outbreak of affected strains, and larger effects on the size of the overlap. The extent of the effect is found to depend strongly on the order in which the strains are introduced to the population. Zipf graphs experience somewhat larger reductions in outbreak size and less reduction of overlap size, but overall the two graphs experience similar effects. This is due to the reduced effect of modification in key high-degree vertices in the Zipf graph being offset by higher local clustering. Finally, I introduce recombination and competition by replacement into the model from the first project. The number of recombinant strains that arise is found to be either very low or very high, with chance governing which occurs. Recombinant strains in Zipf distributed graphs have a significant chance of failing to spread, but not in Poisson distributed graphs. Replacement competition in the presence of a growing number of strains is found to both increase the chance of a strain failing to spread, and to reduce the overall size of outbreaks. This effect is equal in both graph types

The role of endemic infection in disease emergence

Human and animal populations are confronted by emerging microparasitic infections which pose a major threat to public health and the global economy. In natural conditions, emerging microparasites will encounter host populations that are already infected with common endemic macroparasites. Interspecific interactions between coinfecting parasites may alter the host immune response, the emerging parasite infection dynamics, the disease outcome and the efficacy of parasite control strategies. This thesis explores the role of macroparasites as potential suppressors or promoters of microparasite disease emergence. The potential impact endemic infections may have on disease emergence were explored experimentally using the model German cockroach host Blattella germanica, its endemic gut macroparasite Gregarina blattarum and the virulent microparasite Steinernema carpocapsae. First the effect of a hosts’ endemic parasite burden on the immune response and secondly, susceptibility to infection were investigated (Chapter 2). These experiments revealed that the host immune response was altered by the endemic parasite burden but this had no effect on susceptibility to infection with the emerging microparasite. The impact of host endemic parasite burden on the quality and quantity of the emerging parasite transmission stages was then explored. Here, coinfection resulted in a reduced output of the epidemic parasite transmission stages compared to a single infection. Further, endemic parasites had a non-linear effect on the quality of the transmission stages of the emerging microparasite measured as lipid energy reserves (Chapter 3). Finally, the fitness cost of coinfection on the between-hosts transmission of the emerging parasite was explored. Experimental findings revealed that the disease spread of the microparasite within the host population was altered by the endemic parasite (Chapter 4). The findings from this thesis demonstrate the importance of considering macro- and microparasite coinfections, and that this, in turn, is pivotal to improving control strategies and ability to accurately predict epidemic outbreaks