Print PagePopulations of pathogens are often composed of multiple competing strains that can interact with one another in complex ways. For example, strains with similar antigenic properties can interact through cross-immunity, removing susceptible hosts from the population so that they cannot be infected with other strains. Understanding the complex interactions among strains and how these interactions alter overall disease dynamics is therefore of great interest to disease ecologists. While mathematical models have provided insight into the dynamics of multi-strain pathogens, fitting these models to epidemiological data and estimating the parameters governing the interactions among strains has been difficult. Moreover, long-term disease incidence data may not be available or may not be stratified by strain. In the absence of such data, the past dynamics of pathogens are often inferred from gene genealogies using coalescent methods.
Results/Conclusions
We show how existing coalescent methods can be extended so that mechanistic population dynamic models can be fit to the genealogies of individual strains and thereby estimate key parameters. Models that explicitly take into consideration the biological mechanisms driving the competitive interactions among strains can also be fit simultaneously to the genealogies of multiple competing strains. This allows parameters governing the interactions among strains, such as the strength of cross-immunity, to be estimated from sequence data in the absence of time series data. To demonstrate our methods, we show how our methods can be applied to the cocirculating serotypes of dengue virus to gain insight into the ecological interactions underlying the complex population dynamics of the disease.
