Infectious diseases, such as malaria and influenza-like illnesses, are leading causes of sickness and mortality among humans and wild and domestic animals. There is considerable data available on the effects of pathogens within a single host individual and the resulting responses of the host immune system, and also for the distribution of the infectious diseases within host populations (for example, the total number of infections and the rates of new infections). Pathogen abundance within a host can rise after infection, and then wane to the point of extinction because of host immune responses. This project will create mathematical models that link such within-host pathogen dynamics to the spread of pathogens between hosts. These models will make use of both sources of data, leading to a better understanding of these infectious diseases and therefore possible strategies for their control. For example, these types of models have shown that HIV medications decrease HIV levels within patients, increasing patient lifespan, which can perversely result in more HIV cases in the population as a whole. Sufficiently wide-spread application of these medications, however, may decrease the number of cases. Using multi-scale data validates these models and allows their application to make predictions in cases where population data is scant. The models that will be developed can be used to assess how the pathogen load within a host affects the number of disease cases and the effect of control measures such as medications and vaccination on the number of cases. Most infectious diseases that still to this day create significant death, particularly among humans, are caused by pathogens that can evolve very quickly; hosts can also evolve in response to these pathogens. Models that link within-host and between-host data are particularly adept for studying host and pathogen evolution, adding a new dimension to our understanding of how to control diseases with pathogens that can change rapidly, such as malaria, hepatitis C, and influenza.
The central research objectives of this project are: (1) Develop, analyze and simulate multi-scale mathematical models that link immunology, epidemiology and ecology in vector-borne diseases or diseases involving ecologically interacting species, and that are well grounded in disease epidemiology, ecology and immunology. (2) Develop mathematical techniques to analyze multi-scale models and study disease evolution using these models. Develop numerical techniques to fit such models to data, thus integrating immunological and epidemiological data. (3) Use immuno-epidemiological models to address important questions related to control and evolution of vector-borne diseases and diseases involving ecologically interacting species. In particular, two types of questions will be addressed: (a) How do within-host dynamics affect the population level reproduction number and disease prevalence and what consequences does that have for the control of infectious diseases? (b) How do pathogens and hosts evolve in their within-host interactions to enhance their population level performance?
