Towards an immuno-epidemiological framework: The trade-off between biological detail and mathematical complexity

点击翻译按钮获取中文摘要

The field of infectious disease modelling ultimately stems from two sub-fields. Mathematical epidemiology focuses on the spread of disease in a population of hosts with the aim of tracing factors that contribute to pathogen propagation. Mathematical immunology (in-host models), in contrast, investigates disease dynamics within an infected host, to uncover the underlying mechanisms (pathogen/immune system interactions) driving disease progression. Recently, the field of immuno-epidemiology (IE) has arisen. Here, in-host models are combined into population level models so that the inherent feedback loop of infectious disease, between individual and population levels of infection, can be studied.***In disease modelling a tradeoff exists between the level of biological detail included in a model and the complexity of the mathematical model itself. The goal is to develop a model that ‘sufficiently' represents the biological system in a mathematically tractable way. However, given the same biological system and simplifying assumptions, different models can be developed. Thus, model outcomes/results can differ. In mathematical epidemiology and mathematical immunology this means that key indicators and measurements of infection and disease that are important to public health and medicine will vary. Also, depending on the models studied, the results may even be contradictory. Certainly, in these cases, disease models are limited in producing new knowledge about the dynamics of different pathogens. In immuno-epidemiology, the differing results found at each scale of infection can also be magnified, and thus will not aid in the study of the feedback loop of infectious diseases (between individuals and populations). It is therefore imperative that we study the effects of simplifying assumptions on each scale of infection. ***In the proposed work, we will detail the effects of commonly used simplifying assumptions in mathematical immunology and mathematical epidemiology, and we will study their effects on key measurements of infection. Deterministic and stochastic models will be developed, compared, and contrasted. Simple immuno-epidemiological models will also be developed to study the effects of one or two simplifying assumptions at each level of infection on the feedback loop. We focus our work on infectious diseases that enable the study of the development and waning of immunity (immune system memory cells). ***Ultimately, our work will inform the field of infectious disease modelling, which, in turn, will inform public health decision makers, and benefit the health of Canadians.