This research article explores the patterns of genetic variation in populations of infectious agents, using a model that incorporates epidemiological parameters. The study considers different types of contact networks, including fully connected and scale-free networks, to understand how genetic diversity is influenced by transmission rates and host contact structures. The model is closely related to the classical SIS model in epidemiology, and it shows that genetic diversity increases with the basic reproductive number (R0) or peaks at intermediate R0 levels, depending on the relationship between the rate of immune system elimination and the effective population size within hosts. The study finds that patterns of genetic diversity in the model are generally similar to those expected under the standard neutral model, but in scale-free networks and for low R0 values, there is a distortion in the neutral mutation frequency spectrum. Highly connected hosts (hubs) show different patterns of diversity compared to poorly connected individuals, with higher levels of genetic variation, lower levels of genetic differentiation, and larger values of Tajima's D. The research concludes that levels of genetic variability in infectious agent populations can be predicted by simple analytical approximations, showing two distinct scenarios based on the relationship between the rate of genetic drift and the rate of elimination by the immune system. In one scenario, diversity increases with transmission levels, while in another, it peaks at intermediate levels. These findings are independent of the host contact structure. For low R0 values, very heterogeneous host contact structures lead to lower levels of diversity. The study also highlights the importance of integrating population genetics and epidemiology to understand the evolution of infectious agents. It demonstrates that the structure of host contact networks significantly influences the genetic diversity of infectious agents, with scale-free networks showing different patterns compared to the island model. The results suggest that for low R0 values, the diversity is higher in the island model than in the scale-free network, while for high R0 values, the differences between the topologies are less pronounced. The findings have implications for understanding the evolution and adaptation of infectious agents, as well as for public health strategies aimed at controlling their spread.This research article explores the patterns of genetic variation in populations of infectious agents, using a model that incorporates epidemiological parameters. The study considers different types of contact networks, including fully connected and scale-free networks, to understand how genetic diversity is influenced by transmission rates and host contact structures. The model is closely related to the classical SIS model in epidemiology, and it shows that genetic diversity increases with the basic reproductive number (R0) or peaks at intermediate R0 levels, depending on the relationship between the rate of immune system elimination and the effective population size within hosts. The study finds that patterns of genetic diversity in the model are generally similar to those expected under the standard neutral model, but in scale-free networks and for low R0 values, there is a distortion in the neutral mutation frequency spectrum. Highly connected hosts (hubs) show different patterns of diversity compared to poorly connected individuals, with higher levels of genetic variation, lower levels of genetic differentiation, and larger values of Tajima's D. The research concludes that levels of genetic variability in infectious agent populations can be predicted by simple analytical approximations, showing two distinct scenarios based on the relationship between the rate of genetic drift and the rate of elimination by the immune system. In one scenario, diversity increases with transmission levels, while in another, it peaks at intermediate levels. These findings are independent of the host contact structure. For low R0 values, very heterogeneous host contact structures lead to lower levels of diversity. The study also highlights the importance of integrating population genetics and epidemiology to understand the evolution of infectious agents. It demonstrates that the structure of host contact networks significantly influences the genetic diversity of infectious agents, with scale-free networks showing different patterns compared to the island model. The results suggest that for low R0 values, the diversity is higher in the island model than in the scale-free network, while for high R0 values, the differences between the topologies are less pronounced. The findings have implications for understanding the evolution and adaptation of infectious agents, as well as for public health strategies aimed at controlling their spread.