The distribution of Hepatozoon canis mainly encompasses areas where its main tick vector, Rhipicephalus sanguineus sensu lato, is present. However, the detection of this pathogen in dogs, foxes and golden jackals well outside the areas inhabited by this tick species reinforced the hypothesis that additional ixodids are involved in the life cycle and transmission of this protozoon. The present study provides, for the first time, data supporting the sporogonic development of H. canis in specimens of Rhipicephalus turanicus collected from a naturally infected fox from southern Italy. The epidemiological role of R. turanicus as a vector of H. canis is discussed, along with information on the potential use of cell cultures for the experimental infection with H. canis sporozoites. The in vitro infection of canine leucocytes by sporozoites from ticks is proposed as a potential tool for future in-depth studies on the biology of H. canis.

Ticks are vectors of pathogens which are important both with respect to human health and economically. They have a complex life cycle requiring several blood meals throughout their life. These blood meals take place on different individual hosts and potentially on different host species. Their life cycle is also dependent on environmental conditions such as the temperature and habitat type. Mathematical models have been used for the more than 30 years to help us understand how tick dynamics are dependent on these environmental factors and host availability. In this paper, we review models of tick dynamics and summarize the main results. This summary is split into two parts, one which looks at tick dynamics and one which looks at tick-borne pathogens. In general, the models of tick dynamics are used to determine when the peak in tick densities is likely to occur in the year and how that changes with environmental conditions. The models of tick-borne pathogens focus more on the conditions under which the pathogen can persist and how host population densities might be manipulated to control these pathogens. In the final section of the paper, we identify gaps in the current knowledge and future modelling approaches. These include spatial models linked to environmental information and Geographic Information System maps, and development of new modelling techniques which model tick densities per host more explicitly.