Predicting epidemic size and disease evolution in response to environmental change

Abstract

Epidemics pose a major health risk to human, animal and plant life both domestically, in agricultural populations, and in the wild. To maintain global food security, biodiversity in the wild and human health, there is an urgent need for improved epidemic forecasting in response to broad environmental change. Most research concerned with this task is based on assessing individual epidemic size for a particular host-parasite interaction. However, in most cases, host populations experience recurrent epidemics that vary in size and severity through time, with shared characteristics among the diseases spread by different parasite species. In addition, there is a well-established link between environmental factors and disease transmission. Therefore, I propose a conceptual ‘Disease Cycle’ model to link the size of past and future epidemics. After highlighting the gaps in the current literature, I investigate some of the missing links in this theoretical model. Using a combination of real-world coevolution experiments, mathematical modelling of an infectious disease, and meta-analysis, I find: i) the amount of variation in host-parasite coevolutionary trajectories that is explained by the environment (chapter 3), ii) the effect of host-population genetic diversity on the variability in metrics of parasite success (chapter 4), (iii) the extent to which local hosts are affected by migrant competition (chapter 5) and iv) the additional accuracy that is gained by using replicate populations to forecast disease (chapter 6). Overall, I find strong support for certain links in the Disease Cycle, such as the effect of host population genetic diversity on future epidemic size, but there are others which require further study to understand the generality of this eco-evolutionary concept of disease epidemics.