Abstract
An important component of pathogen evolution at the population level is evolution within hosts. Unless evolution within hosts is very slow compared to the duration of infection, the composition of pathogen genotypes within a host is likely to change during the course of an infection, thus altering the composition of genotypes available for transmission as infection progresses. We develop a nested modeling approach that allows us to follow the evolution of pathogens at the epidemiological level by explicitly considering within-host evolutionary dynamics of multiple competing strains and the timing of transmission. We use the framework to investigate the impact of short-sighted within-host evolution on the evolution of virulence of human immunodeficiency virus (HIV), and find that the topology of the within-host adaptive landscape determines how virulence evolves at the epidemiological level. If viral reproduction rates increase significantly during the course of infection, the viral population will evolve a high level of virulence even though this will reduce the transmission potential of the virus. However, if reproduction rates increase more modestly, as data suggest, our model predicts that HIV virulence will be only marginally higher than the level that maximizes the transmission potential of the virus.
Highlights
Understanding how pathogens evolve at the epidemiological level is vital if we are going to accurately assess how epidemics and pandemics are likely to progress, and what the consequences of biomedical and other interventions are likely to be
We develop a nested modeling approach that allows us to follow the evolution of pathogens at the epidemiological level by explicitly considering within-host evolutionary dynamics of multiple competing strains and the timing of transmission
We use the framework to assess the impact of within-host processes on the evolution of virulence of human immunodeficiency virus (HIV) at the epidemiological level, the approach could be applied to a number of host–pathogen systems
Summary
We construct our nested model by linking the within-host evolutionary dynamics of HIV with between-host dynamics, describing the spread of the virus in an exposed human population. We define an overall timevarying infectivity profile αj(τ) for a type-j individual, and model the change in frequencies of each strain, xij(τ), within each type of host using the reproduction–mutation quasi-species equation (Nowak 2006). To calculate the frequency of strain i in a type-j host, xij(τ), we use the reproduction-mutation quasi-species equation, as follows: let y = (yi) be the (column) vector of the number of virions of each strain within a host in an unbounded reproduction–mutation system. Denoting the incidence of type-i cases at time t by Hi (t), where incidence is defined as the rate of new infections, and assuming that individuals enter the exposed population with constant overall birth rate B, the epidemiological dynamics are given by. Here we have used an SI model with demography, as it applies to the case of HIV, but in principle any between-host model structure could be used, as long as it can be described using a next-generation matrix formalism
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