MS2: Mathematical Epidemiology Subgroup: Dynamics of Infection

Monday, July 17, 10:30-12:30, Alpine Room

Organizers:


David Earn and Jonathan Dushoff, McMaster University earn@math.mcmaster.ca, dushoff@mcmaster.ca


Life history theory provides a powerful framework to understand the evolution of pathogens in both epidemic and endemic situations. This framework, however, relies on the assumption that pathogen populations are very large and that one can neglect the e ects of demographic stochasticity. In my talk, I will present an alternative approach, based in population genetics, which will explore the e ects of finite population size on the evolution of pathogen virulence and transmission. I will show that demographic stochasticity introduces additional evolutionary forces that can a ect qualitatively the dynamics and the evolutionary outcome. In particular, I will discuss scenarios where finite population size can either select for lower or higher virulence.

The impact of parasites on hosts is invariably negative when considered in isolation, but may be complex and unexpected in nature. For example, if parasites make hosts less desirable to predators then gains from reduced predation may o set direct costs of being parasitized. We explore these ideas in the context of parasitic sea louse infestations on salmon. Motivated by data, we use a mathematical model to show how a parasite-induced shift in predation pressure from chum salmon prey to pink salmon prey could o set negative direct impacts of sea lice parasites on chum salmon. This shift in predation is proposed to occur because predators show an innate preference for pink salmon prey that increases when the salmon are parasitized. Our results indicate how the ecological context of host–parasite interactions may dampen, or even reverse, the expected impact of parasites on host populations.

Connecting dynamic models with data often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. Here, we examine how parameter estimation and disease forecasting are a ected when examining disease transmission via multiple types or pathways of transmission. Using examples taken from cholera outbreaks in Haiti, Angola, and Thailand, as well as the West Africa Ebola epidemic, we illustrate some of the potential di culties in estimating the relative contributions of di erent transmission pathways, and show how alternative data collection may help resolve this unidentifiability. We also illustrate how even in the presence of large uncertainties in the data and model parameters, it may still be possible to successfully forecast the disease dynamics.

A variety of historical records reveal the temporal patterns of a sequence of plague epidemics in London, England, in the 14th, 16th and 17th centuries. The last plague epidemic in London was the Great Plague of 1665. We use recent methodology to find maximum likelihood estimates and confidence intervals for the initial rates of growth of all the London plague outbreaks for which we have mortality data. We compare the growth rates and consider the implications for the ecology and evolution of Yersinia pestis.