Combining epidemiological and phylogenetic models of infectious disease dynamics

Viruses such as influenza A and HIV-1 mutate extremely rapidly, such that the viruses in one individual are genetically different from those in another individual. While this presents significant hurdles to develop effective vaccines, the genetic variation can be used to determine the extent to which viruses in different individuals are related, and to generate a `family tree', or phylogeny, of viruses. Viral phylogenies contain a great deal of information about the past spread of the virus, but this information is difficult to extract, as many factors can affect viral transmission, such as the probability of infection per contact, and the duration of infection.

This project will combine epidemiological models of infectious diseases, which are commonly used tools to consider how incidence and prevalence changes over time, with models of viral evolution. We will endeavour to make our models as biologically realistic as possible, allowing us to consider the different pathways via which viruses may spread over a geographic area, as well as helping us to understand how the transmission of viruses may be affected by genetic changes in the virus.

Coalescent models are commonly used to model the population dynamics of viruses using viral sequence data. This approach is attractive epidemiologically, as information on the past transmission dynamics of a virus can be obtained even using a single, cross-sectional sample of viruses. Coalescent models are also appealing computationally, as only the evolutionary past of the sample of sequences needs to be considered, rather than that of the population from which the sample has been obtained. However, these coalescent models originate from considering the dynamics of single populations or species, and while these models can be fitted to viral sequence data, the resulting parameter estimates are extremely hard to interpret, as they are so far abstracted from meaningful epidemiological quantities. For example, we have previously shown that the `effective population size' of a viral epidemic is not, as is commonly assumed, proportional to the number of infected individuals, rather it is related to both the incidence and the prevalence.

We propose the development of evolutionary models that incorporate explicit models of viral transmission, considering factors such as geographic spread of viruses, differences in sampling effort, demographic stochasticity and selection. While progress has recently been made in some of these areas, we argue that the failure to consider the details of the transmission process may lead to incorrect conclusions being drawn. This additional flexibility in allowing biological realism comes at the cost of considering the evolutionary dynamics of the entire population via simulation-based techniques, and part of the research associated with addressing the main aims involves alleviating at least some of this computational cost, partly through the adoption of approaches which parallelize easily, and partly through algorithmic development.

Impact Summary

Development of the models will improve our understanding and interpretation of epidemiological dynamics obtained using viral sequence data. The proposed models will be designed to scale up to large sequence datasets using high-performance, parallel computing facilities, yet will provide a simple interface to scientists for their data analysis. Dissemination of results will be by the usual publication routes and conferences, but we will also particularly discuss our results with our collaborators on other projects.

Wider impact

The emergence or re-emergence of viral species or strains, whether by chance natural events, zoonotic transmissions or selection pressure due to theraputic drug use or vaccination, can present a significant risk to human and/or animal populations. Consequently it is important to track the transmission of viral pathogens, both through space and time, and the proposed programme enables this. Ultimately, epidemiological insights resulting from analyses with the proposed software can be used by medical professionals and policy makers. This project may also have an economic impact, by contributing to reducing the detrimental effects of infectious disease on human and animal health and productivity, by increasing understanding of pathogen evolution.

Impact timescales

The immediate impact of this research is likely to be a re-evaluation of previouus results. During the methodological development associated with the project, we will also present new results on the molecular epidemiology of HIV, hepatitis C, and West Nile Virus. We will release software implementing the methods on a regular, incremental basis over the course of the project, to maximise the exposure of these approaches. We are confident that these methods will be well received by the scientific community, and towards the end of the project, we will provide training in the use of the methods. Such sessions are likely to be important in assessing the ongoing needs of the scientific community in these methods, beyond those of our own projects.